workshop_6_detect_segment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: autopep8 in /Users/levan/anaconda3/envs/fast2/lib/python3.8/site-packages (1.5.2)\r\n",
      "Requirement already satisfied: pycodestyle>=2.5.0 in /Users/levan/anaconda3/envs/fast2/lib/python3.8/site-packages (from autopep8) (2.6.0)\r\n"
     ]
    }
   ],
   "source": [
    "! pip install autopep8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install opencv-python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial I reccomend you create new conda anvironment\n",
    "```bash\n",
    "conda create -n fast2 python=3.8.2 jupyter\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install FastAI V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "! pip install -U fastai2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import fastAI v2 vision modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from fastai2.vision.all import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Semantic segmentation with UNet model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall the UNet model:\n",
    "<img src=\"images/od/unet.png\" height=\"800\" width=\"800\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the dataset.\n",
    "#### Note Here I'll download the \"tiny\" version, but you should switch it with \n",
    "```python\n",
    "untar_data(URLs.CAMVID)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.CAMVID_TINY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look into the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#4) [Path('/Users/levan/.fastai/data/camvid_tiny/images'),Path('/Users/levan/.fastai/data/camvid_tiny/labels'),Path('/Users/levan/.fastai/data/camvid_tiny/codes.txt'),Path('/Users/levan/.fastai/data/camvid_tiny/models')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#100) [Path('/Users/levan/.fastai/data/camvid_tiny/images/0006R0_f02550.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0016E5_08137.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0001TP_006840.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0001TP_008070.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0016E5_08057.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0001TP_009150.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/Seq05VD_f03660.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/Seq05VD_f01260.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0016E5_06090.png'),Path('/Users/levan/.fastai/data/camvid_tiny/images/0016E5_06870.png')...]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path / 'images').ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#100) [Path('/Users/levan/.fastai/data/camvid_tiny/labels/Seq05VD_f02070_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/0001TP_009000_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/Seq05VD_f02640_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/Seq05VD_f03150_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/0006R0_f03630_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/0006R0_f02550_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/0006R0_f03240_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/0001TP_009360_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/Seq05VD_f01320_P.png'),Path('/Users/levan/.fastai/data/camvid_tiny/labels/0016E5_08400_P.png')...]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path / 'labels').ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the class codes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car',\n",
       "       'CartLuggagePram', 'Child', 'Column_Pole', 'Fence', 'LaneMkgsDriv',\n",
       "       'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving',\n",
       "       'ParkingBlock', 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk',\n",
       "       'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',\n",
       "       'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel',\n",
       "       'VegetationMisc', 'Void', 'Wall'], dtype='<U17')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codes = np.loadtxt(path/'codes.txt', dtype=str)\n",
    "codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('/Users/levan/.fastai/data/camvid_tiny/images/0006R0_f02550.png')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fnames = get_image_files(path/'images')\n",
    "fnames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Path('/Users/levan/.fastai/data/camvid_tiny/labels/Seq05VD_f02070_P.png')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = get_image_files(path/'labels')\n",
    "labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lbl = cv2.imread(str(labels[0]), cv2.IMREAD_ANYCOLOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 128)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "         4,  4,  4,  4],\n",
       "       [ 8,  4,  4,  4,  4,  4,  4,  4, 15, 15,  4,  8, 16, 16,  4,  4,\n",
       "         4,  4,  4,  4],\n",
       "       [ 8,  4,  4,  4,  4,  4,  4,  4, 15, 15,  4,  8, 16, 16,  4,  4,\n",
       "         4,  4,  4,  4],\n",
       "       [ 8,  4,  4,  4,  4,  4,  4,  4, 15, 15,  4,  8, 16, 16,  4,  4,\n",
       "         4,  4,  4,  4],\n",
       "       [ 8,  4,  4,  4,  4,  4,  4,  4, 15, 15,  8,  8,  4, 16,  4,  4,\n",
       "         4,  4,  4,  4],\n",
       "       [ 8, 19, 19, 19, 19, 19, 19, 19, 15, 15,  4,  8,  4, 16,  4,  4,\n",
       "         4,  4,  4,  4],\n",
       "       [19, 19, 19, 19, 19, 19, 19, 19, 15, 15,  8, 16, 19, 16, 19, 19,\n",
       "        19, 19, 19, 19],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19,\n",
       "        19, 19, 19, 19],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
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       "        17, 17, 17, 17],\n",
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       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 10, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 10],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 10, 10, 17, 17, 17, 17, 17, 10, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 10, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        10, 17, 10, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 10, 10],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 10],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17],\n",
       "       [17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,\n",
       "        17, 17, 17, 17]], dtype=uint8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbl[50:80, 100:120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? get_image_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Path('/Users/levan/.fastai/data/camvid_tiny/images/0006R0_f02550.png'),\n",
       " '0006R0_f02550',\n",
       " '.png')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl = fnames[0]\n",
    "fl, fl.stem, fl.suffix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_func(fn): \n",
    "    return path/'labels'/f'{fn.stem}_P{fn.suffix}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = SegmentationDataLoaders.from_label_func(path, bs=8, fnames=fnames, label_func=label_func, codes=codes, \n",
    "                                              seed=2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look into the actual images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch(max_n=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = unet_learner(dls, resnet34)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look in the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DynamicUnet(\n",
       "  (layers): ModuleList(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (2): ReLU(inplace=True)\n",
       "      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      (4): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (2): BasicBlock(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (5): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (2): BasicBlock(\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (3): BasicBlock(\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (6): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (2): BasicBlock(\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (3): BasicBlock(\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (4): BasicBlock(\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (5): BasicBlock(\n",
       "          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (7): Sequential(\n",
       "        (0): BasicBlock(\n",
       "          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): BasicBlock(\n",
       "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (2): BasicBlock(\n",
       "          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Sequential(\n",
       "      (0): ConvLayer(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (1): ConvLayer(\n",
       "        (0): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "    )\n",
       "    (4): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (0): ConvLayer(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (1): ReLU()\n",
       "        )\n",
       "        (1): PixelShuffle(upscale_factor=2)\n",
       "      )\n",
       "      (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): ConvLayer(\n",
       "        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (conv2): ConvLayer(\n",
       "        (0): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (5): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (0): ConvLayer(\n",
       "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (1): ReLU()\n",
       "        )\n",
       "        (1): PixelShuffle(upscale_factor=2)\n",
       "      )\n",
       "      (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): ConvLayer(\n",
       "        (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (conv2): ConvLayer(\n",
       "        (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (6): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (0): ConvLayer(\n",
       "          (0): Conv2d(384, 768, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (1): ReLU()\n",
       "        )\n",
       "        (1): PixelShuffle(upscale_factor=2)\n",
       "      )\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): ConvLayer(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (conv2): ConvLayer(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (7): UnetBlock(\n",
       "      (shuf): PixelShuffle_ICNR(\n",
       "        (0): ConvLayer(\n",
       "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (1): ReLU()\n",
       "        )\n",
       "        (1): PixelShuffle(upscale_factor=2)\n",
       "      )\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv1): ConvLayer(\n",
       "        (0): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (conv2): ConvLayer(\n",
       "        (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (relu): ReLU()\n",
       "    )\n",
       "    (8): PixelShuffle_ICNR(\n",
       "      (0): ConvLayer(\n",
       "        (0): Conv2d(96, 384, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (1): ReLU()\n",
       "      )\n",
       "      (1): PixelShuffle(upscale_factor=2)\n",
       "    )\n",
       "    (9): ResizeToOrig()\n",
       "    (10): MergeLayer()\n",
       "    (11): ResBlock(\n",
       "      (convpath): Sequential(\n",
       "        (0): ConvLayer(\n",
       "          (0): Conv2d(99, 99, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU()\n",
       "        )\n",
       "        (1): ConvLayer(\n",
       "          (0): Conv2d(99, 99, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        )\n",
       "      )\n",
       "      (idpath): Sequential()\n",
       "      (act): ReLU(inplace=True)\n",
       "    )\n",
       "    (12): ConvLayer(\n",
       "      (0): Conv2d(99, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall pixel-suffle up-sumpling layer:\n",
    "<img src=\"images/od/pixel_shuffle_1.jpg\" height=\"1000\" width=\"1000\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where are the concatenations?\n",
    "<br/>\n",
    "For this FastAi library uses so called runtime hooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A simple hook class that returns the input and output of a layer during forward/backward pass\n",
    "class Hook():\n",
    "    def __init__(self, module, backward=False):\n",
    "        if not backward:\n",
    "            self.hook = module.register_forward_hook(self.hook_fn)\n",
    "        else:\n",
    "            self.hook = module.register_backward_hook(self.hook_fn)\n",
    "    def hook_fn(self, module, input, output):\n",
    "        self.input = input\n",
    "        self.output = output\n",
    "    def close(self):\n",
    "        self.hook.remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(learn.__dict__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function fastai2.optimizer.Adam(params, lr, mom=0.9, sqr_mom=0.99, eps=1e-05, wd=0.01, decouple_wd=True)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.opt_func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the loss function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlattenedLoss of CrossEntropyLoss()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.loss_func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object ` FlattenedLoss` not found.\n"
     ]
    }
   ],
   "source": [
    "?? FlattenedLoss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fastai2.layers.CrossEntropyLossFlat"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(learn.loss_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? CrossEntropyLossFlat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? BaseLoss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the model performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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r6trwSaNYNoqv8BmMVXyQz9AxOfuHexH3MbSqUSJ8dWePdywMAajNrfxM5i79QbHRE2n//JWdPQDeNrc+3i/n25j4mWxmu8/oqzt7vPniGl/d0QWBt80Nmon2l2cK+hMF71gcvqzr2NagEISLCEoRwkv+JhelED/FbzwKa03wRrxYlPbGRIAs04jkZFkWvJFgqKzDifhJU/g5GiJwZNrf4DrTWGNBaZzTGGOay8zzHHDUZQ3OokSYnOhSdXNsXWOWlv1/qwo9NemnZQrvRQl0Ox163QKcjyU76xAdp5CJqwuHtWzwIFW4f749aKb/fP2aSd5yYRWAnMewGIwZTbKMrbHW8kF7L/e6jyJKQm5RIRI0pRR5lpHnGSLwHPeGiZtCKcVh99ENk7vn1WfIrD+H15QD5cPMxtjmKvM85zSfQxnlNYXQnexSFDmvq3+Q1eEKp+vPe031chwhIoADcbxO/zn6ecGS+82RpkR465wP8wFk+lF+/DK/uEsNmFsNopu5XMPTPt7b5tb5ys5e89rNf7vc17X/Pm4DeDnvf/NzVh6qmLzDT8Rx8LWdPf+djb760WTnFbC9QRklLPAzQtUE3kTaaT82BOTiy+K/kFIPIa1gmEIuRcno34bjhsMpFT10F2Z2UZA+2YijOU77MxAl6Cyj6PdYXVmlKisMa2S9Lloc1hiqcoh1MD3RY6JXNPFrf66UOLzaiBP6tnveTHq+bSzKBlGFkK1/XKJhdG0D6Ta8SmKqMdz3TWJ+K021QloRpRQuRgFU/IsCkaApf/9v0FQIL+s8o9vvsbocNbXOer7uNWVHmioKzUSvYLWlKSVXvwf5Bw4BLqEpCZp6ubLa1qBYa1GiQsI6hLRwIXYr4ab0V9Ae6JuZCjQJdj8rCTkK670IpTKUVuggDGtdoxYf8hoFMawx6EyP4rpKUVd1k+9o36hOBGMtohS9yR5c1AwHQ8ywhEwzNdnH1DWDdT9jKLKdTPW7WARb+Zv/z869D655mZ9m4lvOLx7p4Cx8/ETtB1VaxSDfBkbFGotCg/ZS+dquCXCOt1z0norPPHrz8eP2cWpnWDcf4ZNWsThzgLqqyFee88UjUSvWsm/4YZQSsizjVOcz6JBXOcmn0Zn23r9pa0qwxm7Q1CH7A9SmxjrHqexeTp64jeJmA1VNph/DurciStGd6PH7Z57kzj03YwawJItMTfSp65rB+hognN39Wcz0DjSKB2cKnHVMv8Xx1Z29LXMpl5rVj4NXcqz4mpfrGb3U6y7lpdTm1ubnTD96WecZJzFP/dVwvrq2sF7hgK/u6KG0XLZ3ta1BOTc3YGqyQ7ebkWcai0MJqJDIlmhUiFr2SblYtmitRVDUxkDm7//amMYgxIQdbpSQN8Z499taysEQnWu0Vv7YlQH8QKKC++8IsyocWgkq02gt1HWNyjRF0aGY7KOyDnVZs3xxniLPqI3BGEu/V1DkQqYsg9JSVb6kU6cE4lWI8BefGlBXhjrXTbxfBOraIAo/WF/FnJsfMjWZv1hTSocckGlmi5W9ldpYwPLTmWFJW0prkNn91MaSZ5osUzjr+JB8Cq0Umc4QKyHXFDVlcc4GTZUtTYGrRhO/n9IDfkxl6Di5a2nKchu/d+JRr6m8QzE5wYNzHUxZc0cdNFUb9pX3cHHy80FThkFpmTr3JZTSlJO3b/m5fGH+CF+Qb30F1xfmj2z4/b07jl7W3zYfY/Prtnru5RCNSNuwjIs7zt/EQ9c80/zehLsCvgjKUFd+3M1amjK1wQdqNF/Z2WPvmZsBONB5eMvzbWtQVgbroKC2jomeI890U8EhISMay2zbuFDXboxFXAgvWYtRNbiQ+MsydPBEgleOVhqc94xqY1Bao0QhqBAnjx6Ro64tztXhzXtRCdLEjmvncMavT9F5htI5RU/IOznWQF1ZlNbkWY6xjmFZMiwttanJJfeeWeKqQVoOsHPw3LX3cvDsR0Psv6asDKK6aK2barCrEa8pR21houvIcw2iqMytKCy1GVLVa1T1hVCC5fmAuZep4fdQVo6fyx0fvnDcT6ri+9RvR7IMXeTUs/cFTYVcZKMp6/OaoRxZieL4iVtxznHw4MN8X/VrPBPyos+dvB1nfbFNzNu8a98toIQvn3kKnWtm3toHK+x4btZrqrY8uuc4h8ocYy3DsmJYWo4deyNZluNMDa/jknmJOCB/hW/trHy7gf9yjcLm570cY7JVXsbzDIz5/beNyYvYlLFYXx3Qn+w1mqpamrr9/BGy/OHmuVupa1uDMqwqWFeYsOZEiYD2g/uofDPWxo8u0oUb2FqLwg/2Rhw2eiKxUsuaJkzRhC4cIcFvyfRoHUwMt0EsJ7YhiSjkmSLG2nyVpUKwOOdnrg5QWYZWOZlSDOsKEUWe53Q6BYhgrPPelR0VFSSuPKO5SlzcGtY2VCZ4pq3SYTsKGDUv3DTZudKUVcXqur/ftCp8paOOCo25k1GZfSR68z4fIUH0DiteGyIuVD+aJrneJFedu4SmpFmTZVvniJoaZV1CAY345LyzPsoAI031+xMM6gohaIoOiAqa8oU3OEbGL3FlaXIjbpTbFqjKkaac9Zqy1jUT+XhPOue2jANsa1CMhaXlZVZXBdgNCL0io8hllAyNImhVgCEurHA3frVls0ZEQt269wTqtQotGTpTKK2xTQjMNTdzTEY65+O9xsQ3JqFizN/EJQ5Tm2AQHFplWBx1XVOWFTpTkGfkRU5HC33rmDGOmak+RbeDynN6WQHL3jildShXB/H7RAihIUWeZ+w6+kHqidBVQXxZuZ90eHHEMM8v3Jjzk1f6TbSorWOwvMzKqrDy8Az7756g19HElVtRUz+TeQ0ZcXxw8SRZ+U6eXzxGVZZ8pNPBhAIVrykNOGpjuCbbz31Pfgc3Hnncl+u7sDDYjUpoDtQf8cZECYcO+jUqx9zDeE35zhjffeR2jta/wnl1K846vr7raW6bez3WOWrj6LxBqGoDeYYqcgot9J1jpjvFbD3daOr1/DmeZgkQpt9SAK8sN9AOK209wx/Rfs52z49hrvfuOMrb5tY3LKDcih93j40lvxErwl5rrB0ty7jj4k1+4m0sSwvr9Ac5IvDg9JNolTXebMxx+xz4KzQor7/lZi6ev8ji4hJnL5zD2J1MFB16hWZ6qttYLomzrNp7D1VV01SF+Z9Qm8Jkvqw4oyjyDYUvWTZqKeENSVxfEmZl1lDXFcZY8ixDtB9ghusDCPFowS/AtHhRVpVBZ77qy+LQnYyJrKDoT6IUaBGKTpe71qcxEyF+3Ao3JK4cDj+7NrWh08l9FZNWLN3y75l6/sMgkGVQlXXwUOIL/ezr48cNvP7KXf9mvKbmWFpc4uyFszxw4l1MdDsM936Ddw/e2Gjqk1ZjrBd6NXmQp849w2ZN7Z8+jCjh7OpJqpkvISKcVg9y83fk4LJNmqJ5nVTSVG25oJHabtTUme7ncEuWnfNfIXOwa/paXug8ggUOHRryzeoIdy4N6PY0tdLoImMiL/jjbpITRb/R1JEbbuQwfh3aw/L0K/7c2mGll1suu93z28d9KWMyrrBc+zzv5ZXnXl4ud5y/CYCv7nzKe7O1QXgEx21orchzzaN7jvmS9FozGI409dA1z3DHOa8pnW2dDtjWoHR7XWZ2zKC1Zg7H6to6dVVRm4JukQV3KEQV4gLcUEysQjsVCbNKiTdw9GREEfuERYvZlBa2qraMBQnWNJ7LOdDavz7TvkCgKSEOyrHWBFc+ekt+BXyWeavrnEMZi1JCR2v6vR596bI6qHDONMJNvPZsjlL50Ixq8iI+/GOaQVHCZMMR83mjhMvVFmZpa2q5P/SaqiuWpwaUpm40tZGWpsK6LhWrHWmvcPer7pWSlqbkRZqKhgRGenLOtTTlO0o00YJGUxYbXuc1BQ6LDpECZR2KDh1d0NGKfq9Pv99jdVCGQpqkqStGqwQ4hkJ9qDSUB2/SlGppyrU0Fcfxrdi+9Yoopqan6Xa7KK157uQJBkM/a5ruF9iwOtnHdAlX7PMrWgvOqUbw7ZsyGpum0sTFm59mdXxkQxOX8IOIbyeR57k/hrVYY1DN+hFHXdXh5h/Flq01ZLpAKd3ECjtZTr9XMNHvI0OoTYU1lkxp6roKxlB8sjfxmuNDMyCZRoX8nLOWmSf+JG46lIzH5VGu3estzMavstCl19QU3W6X1d2GNz71ECLCqWN9hm+OBmWkKf/Ph8G0ElwrfHx29SQA104c5JTc39KUNJo6UN1DoXLEKZ7LPg2M1rGczO4FoLYGcVFTmb/XrcMay8LsW+lozX6eo66qZpI2e/FBnu716btJ+t2jzcTQGMPqNe/itpU3MNHt83z+aVbO3I61lp36Qep9e5tWMZs1daVCQFeKV1MZ9opw8PA1zyBupKmv7cq4/bwvZ6/r2t8YSsLC71He5I4LNwEP8VLpgO1br6ARLEXR4bp9exkOBizML7K4uML+3bNhRhhWyLrorcjII3AOEdV4KODDFe3QhHOQad308lJqFBZrVuD77DrdbpdOJyfLNHVVIeJC7b1uXus9FuHC3BwOmJ2ewjkJNfJDXKh00VlGp9Mh62j6/T55nnNfb45fe/anEVH0ioLvWv5B+r0JZmd28O5q9lV+m4lXQhw8rZ9J8As3ZlSlcM95S13VCH4AjP2I4kQh3k9XGyNN5dz8vddT/tugqYUVflpZ/ooZhVvjJAuEvRPX88zyfFiM6N/fvqlDjdcWE+3xdV5TGWfk19E6asrHz5/lU1gsDF3QVEaW+XVdXlOxswXsXvwa3SzDTVzDhbl5HC5oyhe8DNYHHK6/A6V9X69Op0OxUtKf8JpyR9/B0rmLiAgXlo7CqX/MYN97mZ2Z5cYjG8t0rwZj8loM8pvPcan1N6+2FHkrbr9wEw/vfibcN447Lr4OGwqcTG15w+nD5J2MR/c+23gvcUL+yJ4+IsLbFwZbHv8lDUpTtiU+XJTnOXXe8Q/XLnQbjlVYtnGlbagsAUaVVxI6GGualby+XNh7LA6a/IpvnezCWpPoao0qQrXe2Ak5rjKO7bX9Vi8+5wKQ5x36vT55J29CI1VVY6qa6clJRCvKyvDW4kMI0Ot0eeH0eXbsqMnzLuTJoFwJ2t85wA8f8x7kWr8LTpqqpDMHP8fh8x8NrvoojEM7r3IVsFlTOmqq0+G9s8dwZw+FBb7gG+lZTuhPM1i+I9zbDkI34uiNZaH8faQp7800K+KDRrUW8sV3Us3ch2O0oDE2CIztykcVkz4+YJ1lf/0RzvL/ALxIU718gqPymPcQazg8+Ai9nkW0YlgZlpf9os0ls8Jz/VvZMbdAJ+82MX14ifLW14jX3GPYxLfcoI5c99D0MfyqhDzT9PtdBL/g1RpLVRmKcG88fM1RvjB/IwBvto9vaTi2NShra6vkeei/FWJuKuxl0iTYY8DbB+bC1YYDOH8zKufDUqiN8d/Re/TvrumeGryZphd/LFMOXg/Erq2jGZkLVSxNHif2JwpGze8VkfvHbQiG2RgRDgvArKMsKwQh04bhoKIqa0xtYON6oMSYecnqXgdIKOZQiryTY2qfNLTiZ1cuzjzci8turxZejqaiDhpCWC+Gjdvv8cWaYtOxWqXCKuSjJGggHEbpYOxohaDDFNE6hyjty/WDkdZhSwgfi6SZENoQc4+aGlZeU1U90lRs0pr4FnI5mgrFK0r7alkT8ngxaR+XUMQu1e4ltLWtQXn22afYu/daZmd2knX6TR+lONsxdYjXtmaBTTVJmClZ47Dih3kJB9BhcZVS4suFrc9vWIePE8dS5HaS3RhMWGUfS0ed8+WhJmyw1VEKrZRP2mc5GIM1cS2/TyCurq6ilCLLfA6mV/SbTYmMAxeKBYpugRVF0S3C3ze+v8S3AtfkQmhmzzQGwpkwaZc4Hsb26C4Yl1GoyMYW8FdZDuX4s0+xZ891zM7uIOv0mbi1g13KefvzN3DT/G5KKcEJJ9Wn2Vf/KQQ4ZD7KkzxB7IVnjcUqH4YQAOeoQzdfrykJmnK+r9bSu4jdKayzXJx9C4jvMLHraUVs5Hpw5xFOyr0Y4zhobqZfLnBKfwOtNCf1p9FZBmH2GidiDsdjaw80oecb5c/Sm+yT5V5T1sHJ7F99+I4AACAASURBVDNkWjPTneKGM7PMDCfZU/d46I7glYSvaLumi6+Ul9PO5XKf+7a59Zdc1f5yPK72udqr9ccR9nKMDMFDu57emIR3jq/tfLrJidzy/GEe3nEUP1bLizT1Y3VoC/NKcyigOfX8Kc6ePcuB6w8wNTkNVrFsoJ34a8ILwVO3xlu32piQIwlN6MQvknSAEodYqOqw2h0hyzOU0sReQ0or6tr3Fsp05m9fB8Y4tI4+W1jU5vwiyjxUjnUnej4cYms/OCmFKI0xwrCswA3RSsh35nQ6OUW3izOGL1z4FTp5zi43y/ff9Fepquqqne3+94eMZsTG8vOHMj5xom7yIcZYXxYeB00j3nUHhoM1qrrCWss/3g9/+ZSErrxXl0EBzalTz3Pu3Fmuv/4AUxNTYJUPq27SVNtts8ayp389VVVxeukEmRttbGes86vgRYKmDL4yTMjynGrWJ+w7i3ejtGLm4n0jTcnbcQ72TS76yVf4QOva4JxBIeRabaMp1dJUyaJbItc5/e6TFN0uJ+Q3EAdaFP3V9zBzcAdVWTXRiM18K1vTt9ecRLY6TywQ2LxOJV7jHedf3jk3n3cr4nPGlUMZKcpPvuvakGe6WRtojcUFTT269yhvPv26Jtrzm4Pfo667WGsZDoZ8bXcfrZXfGG4LtjUou3bvZHVllbIsOX/uHP2JKTItTE9Nttwf/1wXXArvHsfQVbho51BOfE5EpGlc156R+np463MjrWPH6rFYdhhDWrZlOY0JK4hjZQJ+tb2xttlvIi7O0XkH6339kLfxSVAJK/CbrYFE0e12EVwowzQhzCDJS2kRv/fV1VU6nU7YSuDyPqP4Wk/j2jbliiJ+IHVOQojTgZPguY68VxHB1L4qyd8XI0NytU0Gdu7exerKClVZcv7cWfr9KTKN1xSj4hbYWGVjQ/FLs7o9aERC1Y1uCl9amsGX92qtECfNsdteX/TolZKwCNIGTZlR6XGIGJhQJDNaxOx1ovPcV385h7J5OIHF9yULG/CJr3DrdrtIPJ81YRuMVpg80YytKysrdDodOkFTl/UZuTgKA3FqHkNWYWzzLbF8zsyXBUsrp+Jfo5Rga4etbTPWyoYb7NJsa1D27t3Dcn+FxYUlTp9+nllTMdGfYnJqAmHNV9W44JbEtxHzGRLrzmOS1BuIuHYkDhy40ays2SipnVRtPie3oczYWtO4baa2fnalLQ4vtro2GFMjzZZ0PlTWKQpcqDPNlPeErK0x9RDnTY3/4MS78LVWKByD4YA866D1qLHeH3Tid2SM4bE9wutPrzI1OYXWL+H4Mronm4mquA3ehAj8yLNhO1o3Cl25llz8rReSz6X3XK11/PCzNXR90suaS8+ErxR7917Dcr/H4sIST/3WMb7/1mUm+pMUU33EraHVoyhZbyonAU4vH8c5x5mVkxhTo7QeeTLi0KI2aaqVV7x4J7rw9+31CArfMSqGq+LaEK0Uz7pP4Yw3XqY2HLWPMTP/HvSwz+Fdmkfqr/swW9hmmBBi6xRd9MLdgOPMzs+zknU4aN6LqYUb3C08L6dbmsqos5GmspamtlvV3k7gw/Yhpa1CV9stItxqD5NLeQlvm1vnK9c886JruhSX42XE4/xM5njv7FGvqbpmfmWOqekp9GVqik2aEnEbY/Ti8151bRqjHl/o8KXBFX4bhEf2HOPWJ4/wq9k0s0zxl4fCEwdOALFj9qXZ9iodjsnJCXq9LtMz0zz5zSeYn19iotfjdYf2Npav7akQW9y33kBcGGOBuq7JMh020PJPatrPh1kMxBifTz4KQKxaCWXExrQ8k1aJstYKrXToUeRnyrnWjVfUKXyfIWsM4nx7/sXFZZQ4qEv+yDV/lrzosnPnLrCG4foq83Nr/Bt7mj17r+Paa/by5rmX/m7/YOBYW1vhqzNDTj37PL2zll27rmH/vgOX9+rgifzCDX7w//iJ0CNKCc5pRAv/7HXd4KU4fuTZKojN33NnD30OEbju+Q9hVFiQCnS7He/FmtB25CpipKke3/XHcp785hMszC9SFM9x08G9mMFtVGYVPWc5XZ/wRWobNCUjTclo8uQgdMj2+lGhNUvWK0Y7o5qwM6ryvStQcP3sDcEreRhjaDyT+vwkh1fuYmEiLpbUHOFNGFfzrDxCb/k7uWmHYpJpTqztoixK73Es3k2n7LDUW2o0VSzcRV50OXTjCkNrGK6vMH9xjfpfnUbu2s3evXvZs3fPtmGuzQZkcxfdNm3j8FJ5kctdUQ8vXuEez982LHEHyvdue6SNNO9j/gj/bf5GVldXeOGFY3zxtOUDNxxn1+7dL0tTtCI4vtWVn5DpoKmYP6trEzxUhw2aqusaEeG2CzfyjBzjD80+zY4ds3QXbwEIY+4r9FBwPiyllKLX79CfmGA4GDalws753EX78LGJXVNdEt5MU2UCzZa/fge6UejLGsuoSYwDSyhlVEioOPFGx5e5dXJ/+cY6yvXRrK5ZMR19/zA7Ur6A3wvMWbBh5is+WTkYlt4TUpApqEzpO9mWFYsra8zO7go7RX57LHKM9ePRMxyXZxU9k4d31SzoAXMXLyAiPHV9Qa9f01+YY3pqurmZt+MXbsj5i0/7tjk//7ouHz/u2/ZEfvhY6b9T2+rVhv/ejDGNF2xC+22ffBaG6yUAWX4ZM7vXkkZTQq9f8ODcDMP1IX/48KOI7PV6itWHoWxxg6aca7xoHaq1ziyfIC/vRCmF2Xn/yIA4x3DqixTLdwNwdMd93DD3Th+GCprK1CM4cTi7UVP9iR7lYOA1heOYPMYO835qU4N+BER4Vn2DKTXNDTvfzDPnu/idmvx3c3JpFpwlH57l+tkler2STO1iceoLDM0ia7LC4so6N5y5gZlsP2/L/zBw+Yn4l0p6v9zdHLfyUCIxFxI1VZWV7/SxhaZedkm0gz808wzmqd9iYXGeubmLlFKSPZ9Rz/WZ7/8ppl5CUzHc75uIhlXuVeX3vGlpSoV8trOON525IYQ2QfKHMfXrw/2ThY7uLoy3jzEcqpcsdHkJg+JnRoLPbWR5Tl0Zv/VvyzPZeHg3amnfhL9G5YzRq7HOoqwCiaEyHz7TjJ7vJ2FhtfOmEFism0d89+LWCRoRNG0DYlWQEkRplKpx1p8jlls6J6M4dcirmLryfY7qmvXBsAknfLvgZxxx1j8+Y2KtZXV1hXm1ztLyEsPhABFFXVesra6ysGToFkXjMW7LyOsOhgNipj0OpECYwLjm5tcbvOMQLmXk4Zvahj1zrrLg/AZN+bLbWhuEGthCU66tKS6hqZbRCWtY4qTKulH7lCCNDZpig6YImmLT9xZe6OIxw2QxHEeF3R1dOEEsh/Yh8Utpqg6aGnxbasoYX3AkSm0Z+nl5Bx1pan1+juXlRYbD9Q2asvPzFEXhq2O30JRs+Mk1+ZSoKQd+O5GWpqx1TYf1mHeLvUl8CmJ0YFvbMIa+QoMiTofDS/BG3GhmFLpOjm6I0ZljWaFz3nsQF9sk+xvVOl+N4rAbyozj4K61Qou/sZXyC7CiWJo6aPExc/Afiq0N0iGEtnx23j/fNgl5EUFlGaqusWKa+1gphVNCnmX87sV/RdHJ+f6pT1DXGVVdUtYVw2EZeh19++RPrPUGReBFM5tXmqt2zlKWQ36/WGD5woLfyEwpH+4Q7zU8OLXOe5YWmZ0Rut0+sDGUG38XET5xwlBrP/h8/LgJoarRjNwEF7zTyRER5l73GwD8u/xj/A+rKoxzoe16HGid4+KR30Bniv2n73llb/RbxAZN2fCTCL9/qsPKm5/m0PxenHN8ZvYAH7z4LHEwj5+HwyfGfXLVIIhvvdL5rL/HjSBiyRbuQhAOzy7xQkjaH1l6D+T4BpAS+n2ZkaacsiNNGeM15TLfOHDhbk6vnKCqKuyMX1iZL7yT3M1wquij1MDnNcP7PLRjGXCcKTNOqm/SIWe6vpN9g/fzWP3PKeua4bBicfedTO3af8ndDLcLa42LrUJescpqc2VYXdX8pzP7MbXZoKlMPxo09UYAHtrdyp1so7UvLNyItY6yHPLGJ/82y8uLTcsnGxawGlPz5H88Ru+DPWRW6AVNvShJHzSVKY2JXVRUa6uDcA/VLU09sucob7mwBvh9qs6unkREOFQc8evvgqb8ZF6FzQdfYcjryaceZdfO3UxNTdPtTaCkg4iviEIEJ8qnsWPyP7yr6Bn4pJ+3bLHyRzd7VfuB2VrXzCLjSt24V4rSClfV4W+aPNdYG2efGX6mJ2QdRaeTkWcKrf3aFqUV1H4dTKwM8sbP52W01j7BKYQaMH+tnU6Hbreg1+0yXFulrn3cXuE2LKz8tiHE2jff1M456qriv8p5KlOCOPIs5/CxJR8eU4qi6KKUbtbtKNE8uS9nvlpgfv5iYxTaJaDOOWpTc//ECrunu7xjRfs9Z7ZhQ08n50OODt9v7RdvyKmqiuFwQLfb4Yee+F6chb9QrHH8mgoBfv6g4o3fCGXCSijLmpkn/yRaC2W3GuOH+eoZaWqGTHX57oOa4QAe2llw+8Wb+MbKN1hcWeWeheXgswCMKt8cYHfej60tpj4YJmCyYWGjtY569j5EhJNao3YoDI4nreXIwncyHPpwoNaKUyszTQ8utSvs1gd08nykqbBeLC6IjPubQEtTWqGtxkiNAp5fnMHhOKX/i9dUUXCmdxR3dpq6O9LUjotfZkf/erj2T7zos3otVs9vDnltTsZfMqnebCAzeqg2t3rD4EqeePKbHLl4tNFUp9Npxpxs79caTS3tvpPvWvzPLC8vMT+/wPz8HM89dxthsOLAwUf8aRzs2/81Vh59gmLnbvStH7wsTcUpvnOu2XnRhJxzVdYsrKxx58obqeRRnIV/qN7Icm+ACPyvVY3SwtuXhlzTH3D/VEG9NsQ6oSwNW519+z3lnWNhcYH19XWmZ2aYnpqk7HSwVUm8yUcfattcujAjjSWeIbwU3KXG/W68kjCDbQb91grfkJQfeTIEK6mC6+2Ie9zHa2jvi9G494CzlrIsMXWNNbV/ncQZIJSVbfINRdHBuaoJC+RZht60d/3VgDH+PQ3LAUWnoNMpRr3TYlD1EhcdZ/UXFxZYL9ewtvYx4ZMLfg+E4LFJmABorel0cs5m01RVCWFPQAdh8VP87AHnGAzWWVhc4OzFZa67dn/oLH3pT29DNaKMcjTOWnTWAQh5u9Hug4P1oT+vCHUojVXWf+9VWfvmoE6hr7IV2c7RaGrBdJiamqDo5PR6fpV6DD14NmmKUb6xCTfJRj15TY08QK8lG8eoUeiXkS6jpqTRlI8etK+h0VR4fvzSnLWUQ59rtHEhXBC0s5ay8l2Jo6bquKOm8zvAXo2asi1NdToFRdBU+9681DU3mrq4wNkzF7CuDpMzjYSF3Cwfb7YvX5y/huXVZzGmGmnKxehOHAP9cgscDAYDFhcXWDl3hmuv3e83S9sq/BTn+XE+2daU1pBfSlOl//6UUJs6FGP4/VCippT1VWKvyKB0ix7LK8ssLy9RVQN27rqWIs8ph5rYt6sp4mzCdjEG3FiIxoj4lbxhPwcZ7Qvf7FPnLFjVuG7Rk5HG2vgTxTieiglLZ5vzOict07KxJYUNs/K6rnDWV3nFFf3OhV0AnRdPlvl2GNGgdDLNwafnuXa1D3v2bPexvaZYa1gfDFhcnGdqajoM3DnOxcEnDkSbcN4YLS+tsry6RFVXiMDZKSBsrQwDaH1vE92C3sWSosib1h8xdt9u9++cDw2srCzx1Zkef3iwTq/bRSTebi3jcwniMWtj+OFj/rpMrfxeOMH9HqyX7Hz6+1BaMN9hvAdlfKLRt/ao0eirrsqrKHqsrCyxvLzExTpj567rKPKcXl9g3XH9zCJLao35hdGHc+/sAf7QuXOtSZoL97/PEZ3qfAaFamkqfuP++8nm3gmA2XE/IkKxdDeCUO+4j+umDoGDU0snfCWlG33+cb7owuRs39QhynKImr+bo+HrttZRm4rrJufAGp6bn2juBefAnXsHbuL3keDlVrsfhHmCpjKWdr+D/s597ObqaA75trl1yqrib6/dwMLiPN93/XkfHVE5vzN/JOz15Bf2tW/fn8kcRiymtCwvrvILboaqLvlQ/dmNaQG/15jX1LETDCcKer1O0JRuxrFY2RhnhDGisLKyzG99s8vHZ9fpdrtkkkU18aKLauFc2FfIGDKdoQq/qeFj3Wd54+nbqOqa9WqAEo2osH4pbEvgrOMNpw/z5cnH0eiN+w5tYluDcuDQfoalTwqdev40Fy4+zkSvz86ZWXA5LsRZN64d8c3jjPUVYBJr5F0Y0EPMXVm/0raTd4jmxwuFJk/jFz/Z0JLFr7Kvjf9Q8ixrQiXWuLCnvC+dbCcsm3mWErTO0FkBw3VMVeHqyif1laDR5EWPotulrGvOnbsIYqmM7/ehtKbo9nzvoqsIEUWRd7C1MBxUrOkBReHodPx1xqTp5sHbOhtWQ4dPKG5B0GR5wRtxH+6YmOgxO9MLFawOG7aBbW6txluU5rGyLBkMhty3b4I3z68xMx0Nnm5mvJd4R+B8e4+f3lPR7WmyLONHnvVe4j+/uUs5qLj71C+z//RHyVXG//RMydMzHdy6ZTgY8MKBexEldHtddp//gXF91GPBa+oa1lbWWH70aKOp395xN+/tOU7OTbC4BM6ttQZ0x3D6i5jarzDPL76LLNM8N+vbzxcX3kW98wGUtYhS9Ffe0yTgq5kvNYbkdXIratcS14cB4QSOnzs2T20sf+Lw75CXWdP+3oRk7UErzDrF2R0PkD/7Ruxw2PTbFPGb1ums4IVVwdQV4DXlvVGvKdPtUlU1T1Q3sHPlwaApf18VRZcsvzKaauds2rs1Gm3o5ENsBf/5zEEmlnt8z7Wn+MtDMEYYGMVjwWNp40OH9ciANJqSTZoCpTMmJrpBU9Jo6sD1DzUeiq8IHBl28Jq6o/dVTpy4nr179jI9M914QYTdFV+MD0tbY1lZWqXbK5quJHmW0en6MXhtYY5ut0uucxDxURprGQwGlOUApYWimzORbV3luv06FIE8z5iYnOD6A/s4dvQ464OKFb3GrulZb0mtryJwrb2ucaGqyI3CGO0KhLo2KBGyLHgeEnaOsw5nfCWBA3QMq+ENg9aaLLhgMWkFruXCRze+1bo8XEAUplK+fNiJYINbXtd10w/MWYvSmizPWF9ZoR5WOOPIMk2v36fT6Wz3kb3maO37jV133V4fx1Y+dvorO/49xhjuOf1HL/k6Z32uw4XiChvDhq0ckXPQ6+Z0ux2mpgqUjvNeEFTjlY5yKf77Gw6H4Qjew7x44QJfyDP02gUsNHHlif4E7652BS+0OStxweonTjq6XUuWGVDeQ/lLR0tMbTl35sPUtsJaw9KNvwmLMfxXsbS4zOuX/0d6/S6uuLo8lEZTU332H9jHZ+fPoFB873NPwOtvD9tgxwma19RfqYQHW5qKnkq+4D0P1CU0FXKZxln0xbsQEZ5VPh9yfZhD2N2OD+1exFnH/sV3c+Ka+1EX7wRc0wk8hsZumHsXS7IUxsXNmvKhESd+7Zezhn0T815TE3MccxalNHtWHmJ9UHKufxt14UCpK6qpdmuVyCdrvwbqH3ULrtu3lx+zfuL6sNbUoZ+gc47fWTjCX1edDYl55/xY4rB8wN5LacP+Ma75H04cvzXkaDtce+1TKC2cPHEbDjh44KGWpmSTpsrmPLY2uKf/ExeOa+aV4sKOt7J3+WE6nYJ+f4Idb/tY2JAuXFfMaTtHnud+H6nMh1hvPXsDTnxCPu9koX2RocoKCCG8qqxYXlgh35f5bu2V2TJM+RLt6/3sPc9zJiYnEJXhQskchOqqJlkYi838G/D3fOu0MorbbghKxQ87GHFfTuwXQYroxjT75H1ss+1dcB/OCCGdGA5rci3hC2lfxyU/Bf9aZ0fldDGBhviti7U4RPuZ8lYle1cKEb+iP8t8uMC50Z4gMWwU12LEstvV1RUGwyFrq2uj76656UcDcJZldLsder0OeR5KQ2NSpvWRttM0luAxhoFPaRgMhti1Vb+mp/Jx5U7RZWpyinp6Jnh9m+4VidsaKNpNIsHPfvM8Y23Frz0yxviqJOOrlJzx1341bormNaVGmlrcqKnYrbe9YHizppoQcDt/siHQC/F79K2PLAq/CFKcbuYMsWTURwSiXvwsMGZSYnWmxGtvaW0rBxOJTTtD+yPxnrTW2Ys1lV+dmtLa75PUq/0YEq/RhQKGuBYtTn6WlpcZDgasbtDUiyczOtN0uzm9Xk6e61ZIPm4OeClNSdCUa7QwGAywazXG1JwZnMEsvUCnKJianGaqrsmzfKO3Eu4TX7gy0lQ8ptJ+nF9dWcOIYEQ3bex9hRjouPkaW+cltzUoWjL8EkTf0iTLsqYbb8xdxL5dTcgr/OJi/XmItY96ePkL27yoEfwAUtfGn805MmIKI6zirBw60033X5EYwzdNvb3yc2c0QqwSb++9Evs9xfhkpn2cEONLViXkbaI7Lk75WUmzL/fVNePdjIj3Wv78/PsYDIYsDZfpdbt+kDKGuqr5nXyetWoVN2EheP3NAlUXxgQlTE30mZ7qUhSa2OxHYpKQ6M5HN5TGcxTdxdYmfHl+wzNRGrGOclhS1RVaV5RDw7AYBgPuv+2YcNZaU1HzizfmKK34oaeH/OKRDj98rAIlFEXB/IVFvyjP+cRwVdV0OgUHTv8Zsmvzsa29GSdawva6YT/Rfc/1cLVF9zSnl04ED8XymR0HeF/9KYwTTpr7NmjK7HwAg9eLVgorkEsWNt4Kq+pbmorhZsGRRcfSOQ6dvxOlDJnWnNj7ZcQJZsf9OOc46hw7z9zCSWVZVdAVx7KGgRaGxvfmiwEW29pQD+c19fzyLFVVc0z+C1nIwUVNdYtipCngSmrqx91jAC/qHvzJWsj0o9TcigBvnbsZm/lN3cqy5K/WXWweNFXX3PbM/8na2hrOWr4u3wOEccv/gAD54ru4Y98009Ndio6mVgCKQ4dDF19US1OWbP6d1LP3+e7qS+/1e8AL7FQP4LKoKZg882WWahs0ZTkw9JrKdBYmCGEXXK0Rapa/7v8+eXsOxC0hhJ/QXb72wqN+jn3wDZTlkPXBd6CySZ4+8AS7spmX1NS2BmWwskxe9FBZjsIhKkNs3VSRRKNCezblwogUkuNxb+L4AUcj4jdwsahutzXD8jvFtcNT3gvxf1NKN61ZwPf2stbvxqglbtLlGKyvsmPnLINhyQtnXmhm5tY4EBdWkoamdTHnEgyOC8J1zqE6OVP9SZTy5XbP7p9kdscUt7xwdVUOXQpRKuxM6WdSD0ysc/78eRaW5llaXMHh6OQZ3e4Ea2uDkGizEFr2T0z02LljEh9SjzMdF/7fNbOq0VggTT4q62bo2AwUSzlYw685EnbMTjK/sIIxlsFgyMLiUvAmMmKsF/xAODHZ4xPPOZyrMcBfOlY1p/vV22Z4c/cXGQzW0BeEemg5e/DfMDM7w66nPxRWNPutnK8mNmoq85pSNU1r+tYkTc/dBS6UvsvnL0NTDnf+7dgibnonZPN3gWaDpkI/F0TgVCi1z+bvopr9UtCUz3We3H0/u6rvxoljMGhp6oUXvFEMukJGE7WRptRIU3hNXZx9C/WUYSorUCrDWMOwHFKWFd0rnI/P9KMbjEqmH+Wn5E18svUcUaG1k9Yg8OVvfInz587zi9kES4u+a/OfWTlN97oealWF6kdHtvAuut2CiR1BU5qgKb8mKXqEI0357+fcjTU7533FZdbre03huLDv3ZSDNXbMfzloaoqj8nqMsSzmOTcETfkO7dLcT2uPGCAj7zDynkSh1aPgoDvxBhAYrK9x8eJ5qqHja7NPMj0zQ+EKP26+RG+8bQ3K4sVzFL0+ebdH1ukyNTFJXQ4RTOOWE0Je0T2JLqG4ONsf3fgWEDvKTzmHX3FKEInauHCwrk2YWcU1KuE4xq9T8ZsS0eRSYvLLOEs5HFCXVdPePFbA6NxvzFQ7i6vqS8TubRgwQwsDZ8H5ad01j5xm564Sdu3e9kO9GogDihIVjOyQpaUVlpb9YB47J8ca+WgZut2CiX6XiX4x2jo2DuOtWPCL3HkXnxlXYY+CMEqPSr+11vS6HQah1PTEjVPsnI+tfGhCmTZ4tTF0qTMdBizPx4/XfDXLGaIYrA9QNqcqa4bDknM3fI4b5j62ZRXZlcRraoK86IJ1vGGfpS5rTi8WlLNfhDM3NxO0+FlfN3mQ5y5sral87p1NvN04H+YUuYSmgt5M6O14OhN01JS1cP7tuN0PbggnN5qyl9AUQVOdjP2T69TVkAenFFxTc83Sl6mrCrdkW6EVr6nZuQdRSnNxx1upymFI5l8dZPrR5ucfd49t6LJUm1tBhLMrz5HPPMRKcZw5fZqlpT+KMd5YV7P389H8Cc61ZvLZdV+j6HcpJnq4fh57IrAxZ9kOO3t2zn2FptPzzvtxwaDsWrBU5TqidROR2L/8CINhxfmZt1CV5WjXz3ga6+jfFrdKH7XmeWzvMd46FxqEas2hHTexnC2zvjZA2Yw3nD7M5Mokj+89CdKJGYKtP7/tPtz15Xmqcp3OsM/kzE4mJ/qUSqjL9WaRX+u+b+LctTEoQgmjaydyGblf4YG4SZAV8a5xPLmDOnQU9usgWgYl5AaU8hbeho60AEQvZbBOVVXgLNapsBjLocVvDmZroWb0ZcaQggubgAnSzNRE+WsoSzOq4mjla65m4oY6VVUxHAypKkOuO2jtP1Nf7SFNbqvfL+j3C3q90VbJsOkmusQv/rntGVZ0+V3IeWlUMOrdbu4rAQfVaAV4y9NxLvQgk7BhbpNL8X/HgShHkRWsqwHl6gqZKKqqpipLVMc/V5S82PBdYRpNdfsY0w+aUuhl1eoEAY03GAbj7TRlbFy4BiDNRnQWyDJBbRgUDcaG+0J045U3mopFMFFTMScSaMAkIgAAIABJREFUNVUGTRHWrDSaUpg65tjCv9DrLwTFN2nKYo1p1oXFWfmW2d6rBP9x+Ptqg6ayjg9BNppSLU11N2nKH8ttGH/Cf1tseCj84jVlR1ukiw9zek35sSuOdfG10UuJ9wXQFFIQJnk4f2918gKt1inX6qApv0bG2rrRFNtoavuy4QPXsby8yvraCucWF5iYmaWXa1xWtJKGoxCRa2K1ox5KJu45Hz4Uy2iGhWvdP43llPirz5XEJmeOsJ2vH1B0qLpwoRGacc67bzoDpZu95UtjWF1fx5iSwfoyOveVGTHEVtclQhYMSRRaDOsov92p9TO+527awXDPLLvmDHI57aSvNDG/ZCy1rTHWUBQFUxOzOFdTlmsIhizXdLsF3SJnx+xEKPO2m+9vNqvd31ejxxyETtCmGQCz4OXEyZJz0OlkQEGR59x8qiSb6bWuN+61IfzsdYaPHx+S5RmdotucJZqwqdlJyrri4twFFtdf4ODcPVx77T4WjvyWz42JuuoMyoHr97G8ssr62irnzrzAxOwOerlmsj/BYXsL55yfsHzA3ks1a6itcKL+4raaKmfuo1h6VzhDWKwGL9YUcKPKsSH3eL0R9OwCMR/2rHqc2hAKVLymDs6usHNXhsk0JxamMEBpDatr6wynfpeVsqCYfw9ndn/FD1z9tzJ9/j5s1tJUa+uBuMke1lHVfjGkDUUV6jXS1FZdjdveyVZ/r9UQa/9/5t48aLPjKvP8Zea9992+vaq0V2m1kSXLsiVhqSQMdkOzNEzPwIBtMPS0GcJENxFNxDQ90zEx/DMRPRMzDDE0MeMBDM2w2gYCMATbgME2tmR5t3ZbW22q5dvf9W65zB+Zee/9SlUlYUOjjChV6Xvf733vzZtP5jnPOec5EmFidqjhR/JNjymtqWbHEWtPdTCVsL42vAKmDo6j+t0AnFIfAgjUIhdhyr/SYso2ZQI310+ztPRtTfaZuwhT+XxB/oQlSRMO3b8cvjWaAY7l9SUqXbGzu8M4P8to7Sb6w8MkKuP+cY1K7ZXOk1cIyic+Zbjf72G08Z3hdIWzDikHbdaHkLgY+Y+LWLSX6tNCQ8vfECAHIHKt4WZj/OVAxkqYNetcaMQTrd/4+TSfGz0YFycy9L+uap+/X9V1k/aXJCm9Xg9dl1xz1dX0+32GwwFiP363vw9tA+VsfTrzZLLPp0c97hkPSNPsH0Xbq/WS/T8uVy1rrI9TfG61YH9zF+sMaRqD3y5YnQXW1CQJrK6NggT6xW44MWhGpDij5xOuoEl40NowXxRNcD1NFFnmkzCQbXDYtxmQPH2N475JSa/Xo8laAoSS/KsTFf/xOgfUjJYStNa87/kqeFwGccSR9hRp2kfVCf2sz2g0ZKZUKFK1r7kWwCpVjJaG9Ps9Ti2PMA60thzeLnlpsopgt4npNRZssEJfFaaQHHMtplLrhTS7vWYgPE7rOLm/0vzsptU7/Oc7x3PycaQQvCifZJ9VjEi4ceUNFGXJ0E75svwcaa6o6gpj/hh31pEkCb3xRzk3HHDNVVfR6/e5c3AfX6m+AAg29r5AVWt08G60iZgaMzj/SbI7/kvSNLts1tfFvVIu1zvlaxnNfhI9NPeml73n7PQk+SJnNvhTJl96PbNNgZxcj4qYImKqvASm4vCp4ABHzbs5rT5EG0OBU/K3/VHhgmdhzEWYktzCD9HrJbyUfjhgSgRMCZRUjMfbDEd9er1eQxkDIaaakC8mIGCYS+48dzP74co+9uJneei6O0h7CVnWp6gTvnL0DNPrICkT6ko3mLrcwXHFAyWmZyolcYml1hop/A02Lhi02/9F2I0URbyp7p/Iucff68Z9fQp1/NQunxYSIOLnQwM+H88KfL+IAS7RZM14r9rXmljraxuEEKRKUNUlaZrQ62XB6nPNdUslwQlcUFubL+bUkzHzaoPR0jKQhrlSnXv+hx+xL4wQoC4jp++ctwK3tvdY5HnjSRprmsycIs/BuSBdrg5cf2vdi0AJEnlNmsiKCxufiAKisQFTAIQF8NkmogvasHAmk32m0xqVJAz6Q//EO265wAd7PTVifKA9pAN7LTbBaDSi0DPf70GAw2KsRjiFEq+toLyUMmBK4RJ/8EmhG6szeuOtsdY94K+EKf96zImJv9o1JhusdBNoEF2Uhd9tDbTGaAgxGXFFTKUBU5KqrkjTlF4vg6r1K0VUyw2bo3OO+WLG1tYF1mZTRkvLJKQhPqM6F/UPP5pMtSt8n42Y2tpisQiYIna47GLKkiaSLE0CI9M1hLsGm+tgKpQ5uLAzipYBik3NBA5nDc4lHkKu+zmA8EZvlLofNOKsLaYIsayqrKmryhenhvCBbwkBw+GQou6jlMb3I2wxlVwBU1f2MYNfLULjnn5viNUaU9U+eN0ED/GngAN/ArcKv74q+uBGG/PdrbVIJzr1Bn5yonvv8F4iLuTLt3nELQcYH0hDQ3rrOUkUtfIPwfmrQljRps8F3aIszSjzHGcNWZYQM8IidZCmfRASoyuMtUwnU8aTMY/dtsxdexMGgyFCSPq9Pl35kb/P0XUx4xwZa5p+IN16i/a9fn5/be0jrJ65q5kzW9c4q3BOo+uasigYDLwgZkibiL/ebDjdbelAAkZnG/I4dCgpGA37nju3NNpQXQPEWocXB3bs7m7z+SOSwxs1989ixobXNBJS8G/OSbR2vP+o9yzTNKM/6KNkRbEosdpy+NAhimrBzjV/hN1YRVcWbTRJ6iuBX1MjbuohZbTfy7DaMBrEgkb/J9l/gGrpkx176lVgyrV1YZfDVMSJc44X1j/FYP62+AVEhQov3Ojfu7N+L3ZthXsXFplIkoAptXcce+jTCCvI9h+kWn8kYMoLS5pzd1NkGblMSPYeQKUpST1i88YaETCV6orVnc8wGY/57OM38S8HS5y5Y8JgeGlMXeyJfC2eSfRq4rLU9q4mjuTcFzuYaimw+N4Pr93JPJlz/Jzm/XaJdxa7BzFlNVrXlKfuYNDP6A96OL4Un1AwyrqYaY0znOOofhftAxecSj6MwKIkAVN+jZx1v0OSZE28q8WUY2ftHpLdbZ92X1fccMNNRAFXF9qeH3lwBVP7ni6fOPUU3/4N99Ef9PnW2+7lSzvvRzvDH1zzHh7afYa7t97Cul3j8ytf9bItqbpiT5Yri0OasLGGTcYGOWMpJSZUlRMPllAp3+43bcUvwkui+IZAIQAkJCpVbUC4cd1lg5T2c33KadtHHlQAinV+87CB99XGUc0K1tZW6WUunOiuAbFUCYT3lmXFyvIyS8tLIARnL2x6afAoV2ItWZYhlKLKvVR4r5cxGg45+9JpnslLHIKl4YjvX7+d/uAf7lBpTPoD4/Km1KOjBVuLba7fuY9cVn4BSkVZV96qrCqKfM7G+iq9XkqSqLDZiGgnXformx8EVxKaAyb46SE7LAm0RrRMaWgzE9x4YxxKpYz3x0iRYJKhz9xDEOueorn9r0/VXrxuyReD9Yd9wAee0xTmszFaLyOkpKpKsiwlSZP2QHuNjJh2GaMaVhuwjt87dBN1rXnw3HaDKbV7P84KEmNx7vcPYCoZP4DdeBQlJcn4m0C2BbmJkw2uunp4L2484osct9+Kw5LuH+eGQ57wOLm/gthfxW1McCFTzFrH2s5nWTErLOydrK+tYTJvONSrD6Oc9Hbk4c8irepgaoXR8jMNpqqVCjXo84fbP8D7lpbJhitIqfjM0uMBUz3e8IZnefT8/8HkxSJgaonb/9m/+QfHVCIf99lnzlGUd/HFja8ipOCtY5/V9XOJa2iRM2dOs7W5ySfO34WSFR+5+nVIKfnu+jdxMqWuCop8xsbVK/R6WQdTsoVRgym/Lo+ad3eMNwdNWpLjmH4XJ+UHQQhuk3fyoniqwdQt7g5Oyicbe8/3mLEsnX+Yra37uPp1H4X9Dcx1/y1JSMkHL5XThhUco7ssX1p6luHSkDdvvY5k/0Fspfm3hyRPscbPipKrMphvHuUdR14iTZMrxiWv+KRisZt3eQ/6vw4fW4hK1q2r1lXiDNWZF9FPJtA1nbc1H9IN9lsbBSg7VJloq+SN8bUGdd3WhUgpWFpa8vpALwvKttXzNlbGBw0cbQy7u7tBt6byGWIcbHEcU+6EFJRVzXyeM50tmM/zRkyvnTv/Hf/v2h9dMYj1aoa1hg8Mf49fGvwO/2n59xvBwybr6WXvt+xs77K1tc1HTryfrNcPVoVDVxVGFyjlWF4eMQi6Pm2sobV6uk+yXQfhiw/wwqK1vKPpfAXaIFKJKvFerZQqyKO31ce/eGvWiooGtYZev8fmzX/AqWs+6PukhMw/rTUqSRFCYo1mvpihQ7DXmNdWzVCMXbmLOKn7Tn4lOt90+tMR6Ywupo7ZdyOFJNk7jtx94GWYOkBb0sVUW9TrhMBsPMrp8Sqnxqs+BhYwpY3lhuobANhdv4/9Q99If3icJE0bT4jO9bwcUzJgynpMlQVVXfFdx/6Yc5NTnJIf5KT6oL8v4T2pG+0PUlZVB1OLoAfIgZuJWWJ8HZj63MaAz65l/G3fMZ7czGzxepx1fPnIc53N9uCw1gVM7TCeTsl6PX5oepZ37p2CzXsxukQpWNffynL5dvqzbyIdH4dLYSiMU+pD4LwnlKonDsS5nPAeShNXbFzSi3AXNs8uppyD/UNvZXz4gUBltWtASMHsy5r5Y5ok9XHkN164hdeduA5tdMCcx9Rm/hKCg5gyr4CpVzhQulRH51ZEnJxOvnzHSG3eGGIZrdpvbE/pvY5m83LtZ7lAb7WUWvicA0FJ0bh42vjYDsQNXzFaWkKETK+u2qzfC9scbBuCYdY5aqMZT8YUZemlq8sygNu2yp9CBKvcS6bX2h9mVVWTF4Vf6J1hg07Y17X6AWedv6aiaPTGXrY64zw6/969/Qn7+xPyomQwGIQ6DhOSKjRZKlldGZH1Yh3KwYUfvY2DX+HaiYywi3Gr+EIXAF1LoTNETFkOvdGlUiRJW9kevz/W0cQq636/h7OOfFE0KeHgkw+yzHssutaUVeHl1F1cZ6+d8YqYauazxUZ8Pf4tYoxQeA+twVSox4oH08FMzI6kS/woKZo9yeELKFtMhThYxNRoqaGJXxlTooOpCUVZUlYVZVk0eDuIKX8R/nt9wXNV1RSXwFTU3nNfJ6ZsUG0oitI3DbsMpggMh8fUmP1xxNTQY8oZdF3hbH0QU+piTLXYuSymuoHk7ru6xtqB3+s8h9D8LwkqJhdjqv2oVj1cKUVv4DN2G0yFz7TG+sxKETBVFqFkoi28vdR4BS0vELGYTEgMIvSfBuNE01yrsVwbqst6r8SCdqbhsbtS8v6gCHGSsLikDPUmSJ/RFSczTqFrraBIzBhj0bVBNSePoK41RZEzm80oqiJwwwoV04Wda2gFYwy1rtGV731itGY2X7Czu8vhIz1kyETxG5RrvDVj24mfLeb8hTnLN2/mHDt6tHngfi6+fgtZJQk/od8DOOzc4YQHlejSU3iPsqoqHhnMmJ6eUmmNkJLDVx1he3OTSVkisKyuDOn1et4lx6dyd1zEMI+XcjJa281xcEk3yRANmQNCOjDiwCZGmD8HmHBYDy7yuJRS/OuTDqTkA7f3scbw3mcWJJkXp7PGsVjkzOUckKyurrG2dgijBeP9nOteejfZjX2y0DL1tTSEA2GcD9QKgQnL8ZuPaq7ZOs3/uX6MSbLgu6vf5rr8+9EWSm3g/DsbGksLy+/vrPHPb/0z7x0Igdx/oFmbztIYZFIITl31qD/knfdqblibIAC1+yCnlT8gbtCO5zcewTmBtoZc5yjnuHv7dRxy67D2RWaLgulsxtP1Fw5g6hZ3J0+7L3YwpTlav56yrPiC+xJaa+bzBX/wlW/lfbdrbqp+wItFXliwtvJGKlPydPrLmIWP4VljmC3mPP7k49x2y20cPeYx5RpMfe1GQoy7JEnC2vo6p9d3gwE7wARWAlzT3Mvt3ExVVZw8dYLpdEYV1NLfd9XfsM0m851txOTNrK6M6PUy1PoXfN1NENlM9u4PD963CxBAEkQ967VHOJV8mFuc7/KYyMep7BsbUB0172oMiheTD3u8SwHGH8o3uTsI4t9+Xpzj0bWM3krsKU+DSaVUaLrlWLtviA1iuEmW8NR1JynziiMvSOYzj6mx22Ft7TCmloz3c3r9PlmvfEVMXTmGIhJfg+EcSjkvFCAkTvpua3Hr6GZkNTHyxvjxBVsiWMEypApHq9gDov3OaLWJ8BCatpX4AydaW8bYQLv4QHBT9CUlVVWQ5wuKPKeuDaPRClma+NMbSNPML0oBeZ5TlgVVXaNUglRB0jnthXXQNplxwQq82PLDwu7OLnvasb62xtLSMtFx++Ht78H1vLVmrfEcd2jB+mpHCD0089fEuQ8E+eDLhwznz25y4iunMc4LEL5t9T1sb29RLOYo6RitLpNlPYyxzBc5zjn6vQwVJG/iMdJmj8SLiN/ZOWhiNXV4aFHFoCqqsGH6mJlMkw59I5p7iinCF99r/B4Q/PjzlX/egbu9+uT3eYmRgeX6l34IrS3T2/+UqipwziATSXrHn6CKfwUOavPaqcIGsDIBEzEVxDeE4OeSu1isV4jzu94aBc7OTqGNY6N/QziU/c/PT0/xvesSsXfct14INGzElBQC0znBI6Zu2TkOAkzwJDCG69cm/rN3Vji6eT8vHn4EcNy29xCyqulxmjSbUxY9nur/LZNiFjC1TJamJEqxvXcNy0tLTXV2nhfsFfstpqRf8+++Jmfz5oSb8yBQiOCk+CCV8GzADfW72HO/SuyLtLuzy976LuvrHlPRgvFY9GrZ5u+AqQNpxsGGum/X9zaxzvLIqO3xE+Xs37b185w/e46/OP2Ax1SS8t3Fr7O9fRP67F30ipzkps9zm/pRTGV5dvHrOGfpjz6BShRH9TuJ1tlp9wgA9eojzTUd0+8C+XjznLqBewGckh/ymJqWbRxSJIhE8AJPNqYggJO+in575c2dJ08L2DB3CB9vS9IEnNdH7A/7DIaKfn+Irr3C8OHe1bxvukAlgl89MvLZvgFTl9OHvuKBoo2/gOAz0OTsXuK5xRMyVs82x8LFlF9nZ+pWBQtal5kg2+03zyCvjmw8oPhZgT1sKP1YgT+ZTinLkrKqsQ56WY9elgZRS0uaps33KqlCGq1lkGUM0h69LGtyuKMHJkR7fQcOFPzGWeQl83rBYrHwiz8+wYbGs00jqujp/13Gwff7g9hYg629OF1VVWxWC86dP8/e/j4raxtI5YvLisUC5wxZqnxQDTDGUdcWrSvfOS+5giV/0bU2lEq8xfCW6EFWZQ3SF5+q9CCHGyU4ImgOUjPx09svivSKlBKjje8mqRxa+85zWlvyIqeqPeCyJGMwHOAWzqe3f53UyN/30MbXU0nRwRTQuvgRPwfX24GJvhhUzf+21JanDFtc+rkMrSEipoxDhMSbaDiExFSPKWg26el0ysLllFXVwZSvw1K9DDEcNs9USYU1rsFUFt7bsz3Kixb+pTHlb6vIS+bzl2OqG2P1bIi87L50xSFaXMUUbWcNdW2Yzwv29S6bFzY7mDqETHyDqWKxQAVMqWAwtZgqA6aukPZ8qZ+5g/92cX6spSo1SOd7OqXt3HXju03WWIOp+DkHMeWC96IuwpRzoWC8tuRFQVZ5qRaVZAyGfR9m0OaKdOOVpVfKmjRY9iAQTdwjdkhsT9NYse6C5d/MmxOhgNH/2DqfKmxF9Ghazt1ahzBBh9VBkkiiTJcz1ns94b1N6powDb/o6x4s585fCA25HFJIer1ek3XhK3LbNNvhYEBZFAgLbzxZcm/vnSQm48JwCNLzkfGAtC4+4M69B1qh1jWLxYLpdMJVR64iLnxjvR4ZMaB/pQn/OwzviVUURcF0Nubxw3Dh1BY727vkVcVa6PH911u/zruP/CT9XkKSZJ7iq2uMEQiRUhYzhoMevp6mw9OGe744wNv8LeK/GvcpHChQagfCkThFP4k9cjz3aq1jaH61eb/WGmf/rc/Ekxd7XYJTt62BgJtemGCwCOGX80vX/w7Dne/EWs10MiEv5gwGI3q9Prds/QiVrlHGkvVeW/1r8rLmgWlNohRfOjxEhLn55rUT/Nm5a/iB3VOMpwvypRZT1lrOTU9hDz3qY0prEjU5Hrw8Ly0uhcNawVEDzx1+mFibInfv9zVHwFeWP95iCmANhA3Y3AhionjhwxcPP0JSaiYb6yyvrfDs6TOocYupUf521MoXfKfMRY90NAIg2X+AoRnQ720iLVxVfBuZ65GIjGSYMTyRklzzOEJIvnwERoN7ffvq2cc5FQL1L8fUlH8qHuSLh58N+DPBMBEvixG80rhc6vHdm7ci5dMh+7Hg320eZmfnK3zywlvZ2dkjLyt+VP4eWmp2nENs3UPWUyQqowRO7j6HMYK0fhvz5M/4huwHGCT9AwbA7vp9bOx9jj9+8bsA+MmbT4F6ovHaEfhAfGN4CZyJmPL7auIc/UQ077Eh0ULFeXCwvvtZVvQa6/Jq7t29tRHw/dyGL0a/e3MGAh6/ZqXBFMBGdg37YoK1mvO9o+izn2J6R4+VlRX++Y2WerPGvAKmrnigPPfcC0glSVLF8miJpaURvSzzKZm9JdJeTlJoal36h0s8KUOvBWFx0uGcJIYSlJQQNlZr2oZXMnRqjJuTz6FXCBlk6WO9RTh2lQpqpgaMShDCu795WbE3njIaDL0oofC8ozY+R9wGUckkSciyjNXVVVafvEA+nuL6fVS/j1CSo0/ucOLOI6Shl4SUQaAybKBJIgPjZtGmJktSnr5acmG44JaGF/IbtHfJ4yH293WkwN7eHufOn+dv0zF6xzAYDFhZ26DY2sE6CcKSJIrhsNd4Z1VZsbK6Tn+4TJYNefrJcScW0pCNjcF8sVfZeIbOAZIoax+tFuMc0/kclSgGA8UwTRHCqxjHiI0pTXO4GGsZlR8gra7Gyp9u3f2YyGFc4/5UVe7ldpzj6PkfZDEoyfOK8XTmayQSzxMT+rHEwtzX0njuuRdYH/tC2tPlYZaWljym0oSkP+KWq2vG/ZKvnH8II0qkgPOzUzj84SBCdla59nCzknqThzymnONFYbhx6wFOHn6kibkkuw94mkPIRmQTHHbjMxdhyh8mHlMqYMq+DFPp+EHEQMD2fWjnsLbAlYWvlFeK0Y1PsfP86ykmU5YGlqTf95S3gQrfJliqhMP1xxjVfYo8Z+Z8E7sWU5r8MY2q/4r5eAP5zdcCA6JboVTrAVwqK+ty477d/LL1Kx/fu5Vz585y7vwFnn/hc2ij+ffJiGR0iN9/9m+x1/rv7k8fYni0x80bc5yzfOWle+ivDegPlpFXP8b4ScEp9WEGqs8x824ifu7efh2n1eeJONP2bsBxdnISgKOr4+b+AI6ad3KCDwZMLQKmJMO0Nf6Uj6Jg9EFMGWubwyYK7G7sfZ699ft43j7hvTH3AFWVs7L9KM7BYPhebrn6Dezu7PHi2Z8lU/f7XkvKFz1GTGVXwNSVK+UTfxIZI5jOchZFRaIkaaIYDvqkacLS0hLaCGrjUFgSBL3ewGcThSCdNW1APlIecTjrMMJgQ4EjhPdKr5LbzQaJhTw4QqMm37XRGIPCU/om1Ji4kL4opW8MFTOuqrIkTX3HQKUUi/mMQelP3qosfaaE9ZWjk8mE2WwW+N5gGQVdLyklqZK4RDUFZDG20xB+gua1v0vMpDvaFOE29mCtY29vny+sabY1mF1LL+uTJL3GC9J1hTUa6wzzeR4kYrznF/n26KlFJ6z1Tjred0NDiea9nad3EaPkwjVbZLB0G6amc8ASLC9jLNYaFosZ29uCh6/5X0gz31Hu6pPfj0BwJtml1jXPO8PcLlgSv8FwacT97t8hpVcsXtQztMu57qUf4KojV7H90Ef4Q/VukjThJ069tigvmWQIYdEG/vjMdU22279XX+G/6R2hkm+hN6y59cineaL4PE5bkq1vpNfr+2yiWKC2czy0kRA46TDrjwCCm3eP45UCLNYJ3KqPiUghUdOHGgolphHLINNys72Dur4LqSTWWJ41vxYw5TO/btw+ztb1XwQpsRuPYjeuRTuL1jVVWZEWKVnm6BnDYj6nHH6C0i2w5+8hkdK3KJCKyXzGbD5DCIlZ38Pa5YCpkB7eYEpw002P41wfYyyfj4eAaKu943q91CHx5q3bAJrgOnhv5L7dnHu2FwB84dCw+cwvHnqWdzz7F5w4/yLb2zt81XwbD549wbnXfxUpFbet3tLBlGU+z3k696n41y4fIxdTpPJe3s3uTnbcSZyDU+rD7G3c11BPG3sxjcYFvS6H4nj4SRjhH6fUh5peVi2mPAf/gngyRjt9lp1wl8TUn3/1dxtMJUmCOP8Sp2e5N65feJr5fM52mjEcjbjr5qf56FcrNi9scdXiu0F+hn1zC0kmwXqPJkkUx2dfa4OtJGsWYFFWmEXuJ0AKVlZGjIYj+r20qSGQSqCcI+v1MKbupBe6sKnETTXqbgVL1+EnRITmQLLV5YqLX1iBDZulw1MlCB8jMMYgXVxsXuo8nlrNQWb9ZJdVhcOnQlprWcwXrNQ+g0tbi6lqXApCwWw2wxiftpgkXpU3SVTjiSklSZ1EChWiQO2h4VznYf8dR3fTjryoQDaxC2sNk8mEncUek+kUKSWDwRCEV5pNEonscOd1panxNFQvy4JHEg7d5ov8z2LeWPcaRKC3XBMba4zaAyBw4Rfbau6Y4eWaA+XAoRKuz6dDVywWMwbDAb1eD3P2FALJGdGnKAqKKidfFGz0T3P48FWw4TBWU9clRb1gUY3RtkQmPlNPpiKUyry2DpQk6SFUkOgoar++hGObXUajCcOAqXyRIGWCVAahVEik8LU1TcHvAQr1oFSKL+Z1jYyNC/XCsTOpC0aXlW3GZK01KUljpMlgCXQ7mEIXU66DKV9PZI1lMZ82PZKFAAAgAElEQVRja+0pTWsxde0Nloswhdy/DKZcaLsQ7641JAXia8vcix8R4wt0qFUHxhom4zE7u7sBU8qnBqukgyka2rvBFA6WwIX96iCmQr1RfCauY7y1b7mIS74UpmjovZdjKj77V8ZUlmUIJ5jP5h1MlYyWlz2mbnEYW3tMFRWV2UebEqm8B5SEQnTXeTIXjyseKGVdNHylkop+bwTWK1/OdqdQaxyCfDHzKqUEyzdq8DgQIvbCaF05Y20TRPfS5KKZsAia2rkmUBYDvsbYlhsOgThn/ecpa8nSlJXlEYc3NkJA3oVCRIkSgsSBSipUkiJDVkitvdRCNhwy2lhjtrsPWmNwFIVpKJYIIq8gmhEBrKSXz6i1a9x1KWJTm+5q+buMdvNu9IUk1Nr3ldZa8x+vM9xxpsZav0klScJ0NmVRLNjYWOWaa4+SL3J6Z4YcOnINs+mYIl80V+QL/7zmUMwt99cds/baa2kODhdNg+YV/3dASUxV7fUHZL2MNEmboGt8/EII6v77fMV4ZknLX8JoTV3WTMdTLN4oeEH+bwgExcn3NrUR1jmGrqIqK7a3t9jZ2mZnf5symwc3X1PXOfu7m/zAuZfoDwbo0dLXMP//cKOoc4pq7he0gn5vCA5UPSAfP4yyQxywWMxg916UE1hJG/dzkO4/yEGtLxC7b0UiOHHoYYSQJOFAz8YP+kI0AbXQxPogIeBGeydSew/+5HiZeuV3vYeC6WAqYWV5yIXXPUNSZR1MCZRIA6bqgCmFqAVu+61k1qGEQF2XeUyZiKmSa2uPqZfKu9kXj9Hv91lafDPHlo7ylPgVlBT0ez1qHcoPnOW+vQKvJ2V5dCW94hzft5tDoxwcWmPjuGdngUPwM7zBY3rXy5NEyujuPKeqdMBUxq23Ps2vzb+HRT7nx9fHHL7hBvJFwblzAVOTDqZcwFSRgxMdTPm4cRdT33TvJgI4uvVOTqkPBaqL5jqhNc5aTPk0eI+pqOMnGlxJpUiQSCG9LNMlMCVlKDTo1BudOHEX6+vr7F99FV9aKxmVe6zaCR898SyPHL2Dn9u8ievUNSRbKc8eO0O/P0DX6mvL8hotLdEGfxzTyQxdabCWGw6v0usl5FVFsViQl5U3QUTSZBg0tQmiFT3zyr0aG3lQ6UXmwLUBNoGvfwlWkCBmnNAE94XyFrt1FuEcWaJQEqwzVHVFv+/lzqsgXCdC6+I0SVu5F+FT55zz1bdZlmKrKmRPSYoypCeDl5+2lqooqMqC4XAQ5sarxSZSoYRfSLP5LHx/eIDW4UTngHwF9ssHYg0fvOrP+aGt76KuazZ39/hEuksdlJPfVGlmRREWsyXPFyRJwsryGmlv4HPhpeQ7j76PwXBIns8bV7ssS+rap2Y2ZpCzONtSjfG0EBct8u7x2Hox4ZiyvvFZVRXe8lR+U8P62qUYyBchJgUCO/xxsuKXSBKfgWZsy/tKAcP0QwghWcj3UlU1zmr2drb46/5PM6sXFP2SsqxwOJ4YvJ/n8z7Z0xkvZP8zSZ2Q5in/go9cecL/M46lpWWWS5/s8T+5F5lOpuhK87h7guNr72M0eIaiqigXC/blwwFTinq1plYTjKlRysvPmCYZBSBocdmDmBKxsDZ00BRCcIMJGNpdbeKMxwScnj5IveblWdLdB7jdKcbXfgXrLFfn38vm6A8BQVVr5M79Xi/MWKreRxvrOWLqhmwTYQzZaMik2PaYGq8zy0vKYMVfaxXr5g3IXIA8g2CH3bV7McaRJilHxl9ivHEf9dKIT/QMvX7fI0rrxgMDwefWXx5oj5QXhHqS0Pjvi4dH/NTOk5RVxRf2Psn//vw91HXF97oPc+7Cffx2djcaw39vHidd3MQPTc6ClKT9ka/XkoL+YOAxtZjjBJydnmAwXPKYmt7J1vg0g1WPK2ct9+4umhyWF4Th0O5nccAp9dmGenTAi+LJl1PKDaZiW1+JEFnAVOvqCAiYkp4pCp7fpTCVDr2qhDGOO+541seT05TPv/Aws8WCoiy55rqK79+RfCb7AwbjPreqH+H9JyRJWpCIjLfffun1fcUDpaupH91Day0Y5wPziSKJzXGs7zvvZOwv3anXiK64o0NDxEyFSGLFg8c11ldMYWyuIU5g+OPb+np5hljFW2tNrTVZ5ncwE/W/Ot8RM8IQAqkUVgSZ6OnMB++dxTiNUiF12PmAvKPTya5JHhCkqU8zNkZjtGY+m5GlbW1HDIq1hNGVD5Um5hL+1lqzvb3NVrJLHegFX9lsiZ/sakt/MKTXH7QeIj4NsJU+D8/QWJz16qjRY2nc8IbWiscPwXWPqaWi80p0551XsI3vjRx915fvHj6i8/Nww0JKUimRcfHbNnaG8POvEomrfKxL5yVFXfnOm+FayqJEV3VoKSwaTvu1NJIk8bJArqPUEO43Ykpp2cEUOCm+dkwRjnDRrv3G6wzX0KhHuXAtJmQZSdHBlGnoV2OtbyvsLoMpeRlMmSo0hvIAVlLisBdhSjSYGgyHzJKkwVQaMOWv9aDnH+srLjvi/Qd6SGvN1vY2W9tb1FXNlttGTSZUKys4wAiNqy1SqYswJQ5iqrMvOudVvP3dhDmk82yaV2hw09D+7SsNXETEUIMp276H9rubB9197VVhyocHfLLOpTBFg6kxY/ZyL4G/6b5WteFmIYYWkUmCSizg09SUjPoxCWnqg4DGidBm1FdBNzGEePNNkNovdIenrzx/mmCbAJ23lKWTxE6Klm4cJuj6GENtHS6BShtsUVFWGqVKAKraNPEaG2iThBYAUkpO33GYfDZj7VPPkvYzrHD837cqvkv1MVa0AetgDcrONSiVMFxeYnl5melkTFHkPHWN5P6FDg/LWyBxB3AidEi7wur31p7kR3a+B4ulLEu+sFaz2CpCNocLbXz9e631nsBwaYXBcOgt+TC3H9v5DX7w2p9qFiTNRt/WHrTEboez7tJbHYPCifYg8enjdN4bP1C0HxMPU9cmK0QQpeUveyrT+sM/SSSpahWfq6qmrr2FLetfoBc9X+sPUBHWTjQunBNYA5WuaDRGX8Eb/M8/Wr77lHiSyex2qqLizO49qBt8j5gGUzbBOhkwpV81ptTecezGpy+PqcjDW4fF+jiLgOsMPK89zVusPoKbfkuDqefsbzIsfbFvVRvM1Z/232MPYurY2pTN4ghmf41iPmO+s0vaG2Glo6Jib7nXYgrB/nhIaJVDuvwlNvZypExYXT/E8qENxqpHUeTs7++zvLJyAFPNwRkwFX211jtph7fuE+7bL7BAVZb8Pxe+hcViC6M1v+W+F5NZ3n3+OaQU/OXMctvakDfdvGB5NeP5zRS1dxxVFiC2KZc/ibtwa4sp1x4Weu3T4IYHMOUaTPn/62KKoPJ9VL+L0+pDB4y4g5gSHUy1NTseU67BTTQMXglT0bi4FKZOnXoTNy2D2juORPAzayUi97fzHyS88zKr+1XlVDra4Dj4vbHShtpqFnnpMwbi5DmC7pTPiFBJ2xsj9jnpWt+NQmqi6IWJkbJBh//dUK3eyrS7xiIw1mt5mSRFG4fVfoFXVRR3DMFC21rMXX0x8IHDRV3z89eWXC/+HG00u+Pv4M/XBcsrK2S9Af9FtkGe574NalmEDTsmG3gxvOWVVZaWlhiPx+S6zf6y1vJbR/4UgeCHd77n1Uw5zlkubG7yxFWOcTKmrEqUlFhhERZWVlaoK58GbZ2lNhVa+54hXQXoWtdsbV6gKhfNBmSMReuCsqiCxQKdHan1K9xFf3f+0yzieMEh0CuEZG1tFQJnq+sS7yS0gbyk+ICnGV3bBA3AOkmSROvdopQgSbNGd6nWOnhXjgwZ5t+1fHLQprIGRPRmX2MNtrrjRvMG9lf3qfo1z+9J/vrkE7zthttZFAVfNX8eMEXAVI05f49Xaj78GZL945j1RzpWN4FOFdw6zVGLt5Akis2+IcqWH3VRX8o1mX5+I4ZT0h9WN9k7qIzmSf15nl1/mKEZYYMR08VUNn6I69fG1HVNce5BNgcpaZbBVSXHnGDiLPO65tx8ylq6hq5riiJHHVpmbW2ZrDdAipQjvQsURc7zmxmvd3fyiXWHlClr4iS3cBfHqjtwGB75xFc5/F8faTwDE+SXECCvIEnYzQCzzvLo1t+wu7vDeDzmu8sdfk3+M6wQ/NT2aYajEXU6wBrDiy6nqite2F5htR4CmnrtYeq8QJ+/mb2v3kJVzIloiZiaDz6BM/YApl7kKf8u4Tfvm92dAVSO58VTPgzQGHldTLmAKcFh9x3owaMBUxW9yQPotUeC2SiaxoGxNuWY8c3STrunXzWmzp18MxaHxTG8W/HCl+dNluw942caGuPtK5ePS17xQFleXvVWvfVxiSRJ/YYdellUlSYva/Kipq5rpPLpUV6bywelhA12gxD+ICaYI+HE9daqwFpf2BgbBlrTdtuL7noMbsWzwIRslEiFNa63iNPc5iOI4ALGR9cO0WS11LWmxG/SJjGUVU1W+Rqbj9htrIXcLHiHS4ISarA+goHvWwtLpuMxZ7YmrKyssrZ+iKWlZX54+3uae3k1w1rLF9c050+fZzafd5wIgUqC5lmW4KzAOU1lewgh/QFjDEef/TRlUfDQ8jsbKfLohldlQZFXzGZzRsNe4zHYDkj9fbkDfx+YO9GhvjpGlI+dCNLql5t5N4Mfo1f9SrDKfDZRPMh8zNUG4VB/ndF6cxDqZ/znJEphEFhnqLUhOuYugLcbu2soOnPwaf9jj6XlFZ4eLmGt4a4LE19sqi3vuNHwXOF4zv4WM70gLyrqWiNVwlH7HnZ3nqBe/Qu0MSRWUq9+yjOHUeOoOa59tbcTYK1sKS0HJ4X3VI46j8kzCsC0cRgBJ3aXqKoaVdxPMhohVp54GabU3nFYgzOTVapaU8jcB2njVK9PcGPXYqqqPKZMxFSBEHDz4T2s8Z9drj7Cs26Ec28EYGftXlztuLB4CYHl0ManWDz2NMnKKvaWb+9Uzl95xFRhgNpadp65lTPll5nN56jd4/x389MYayCIKwrhMfUd6QrP4yWjrDVcX1lOTicURcFi+HEY9iF/S7OJV2VBUVTMz9/O/I2CZfNM4xXc4u4AEWMkjhd4osVRxwk5pT58kDXABQpRYA4/inTWJ0U5S7X6cCNL7VwoEO5g6gX3ONaBqNyrxpQ2JhwoHvNLb055y87rAMHfnn6SiPVoAF5qXPFAGfSHjVyxlJLRcIQSilr6TTbSSCao7ionEarV2yK4pn4j9xfTdmMM3kLY8pvAPa37FrvtOUdIywtZH3g+44DyqejwliLShOKAskWrgHuQookV7cYYtDM45/u+6FpTVSVCOBKrUTJrrk03nHK7gUXNr6oq2LqwR1EUpFnPCzGqpAlcXjy6VKjPqgNda6bTKds7u+T5gpXl1ZBY4N8nhSRJ/f1Y43ABmF7S3zCfTrxsuNwgTX0qaNz7o3povlgwGvXawyR6bgc8ksZNbOYK56kvv37bi/eHtudmpQiUihNB5iMYGdZ11KE9Jxurwa2LNFxLx/m/g1EgBdIJnBQhzuUaqxzngdBYegcohtfO6GKq36sYDkdIodCyRBShSZZxHUwJTFd5O1AhnjIXIGLMMZIfHeOY8LyaZnW+TSwRTwRMETCF6Mgn+eu9JKaaD2+p4PjeOGwIghtjvFxHoIa6mCrLgkRlRMv85Zhq6d+qLNi8cJ4izxlcNaPX6wWdsMtgqvMPb70HTE0mbM92yfOcDev3psh7+NoyEQxcRxruz4TA+Gw6oSwKqsoXpsoYnw0belkU5PkcxFJzmAhhsXjsWdFSxM2DgubZ2jjXnfsQwq97pfAFuw2mAk3194ypyDRF7Ed9ti5Nbr/WA0XXpe+9nvVJlpc4cuRqirxgNp6S1Hv0ewopHUujPmmqcHi+t91cFUrJA7nLSQhoxUtKpPSyKGmC6YqIxn0tToJ1TXZW/KGSijRxEPqi/N5sC1co9pnRk76a9Nvl6MBGq6RPrRMBPF7yxTYa/1LK0GLTLzZjfIaFB4HCGPhT4/ju0oEVKJU2p318or1ejxdvGpGkgl/qbTESC356usGh9UMHZF8OjHBQ/WL/w9R1xUMb72agBz7Lw2h6vYx+1qeqa6q6BiHp9RMElrJ0EKg/qhIJjMf7lEXOn+z/Kd9x7Y+Rpb63u1I+4NtLE/r9DGO9/pAQhjTqeYlwiHcfR7g933vDbxZ9/Sveguu/L96Ct3pCdonWBlNp0uIDwUvyvTYif+vn2oO1SZ6w8oC3YU2QKo/0D8EjUbJp+KZc8F5F9HptiKGIgzfxGhi6qrh/qn1wfuNQwFTObDxF9iw3jO5lJgp2Rn/FYP5NuFqy5c5Qrz1Mdu5BrDUkAVP12iMNprLxQ95YWH+YM1c96o2YNEHuPnDg+52DUzLgp4upoNOnpCRVCn3ks5RpiilSnKlIEuhlPvbnEo8f4/yz62Lq5P4Sw+GIodvBGOOPKimbpAKv3O1VsRcveUrYmAyxCkWZc+icJEtTVtdTxI2weYtGCkfvhYz5dMa+sbgnPsLe0W/hmquvYWPj0IGOpe2N+v+8ZXvOU2ckde09vvTaL3Hy/3sj88Wca1+XsZ9Jqqrm6lJzWgoG/RSB5arCQW24pjJkrsIAk32PqdrUsHUvWZo3mNJrj6CGBclwzmBnSD3oI4QmTSTP8VhY06D2PVUFkO4fR69/mq/axxq2RYh4sCXN8xJC8Dr1Jp6XjzeYsoLQdvjVY+rEibu8MeHi4RA8mvA9KNNgavpFn079CfGkN1cCphpL5TLjigfKhQvnmo0yBvFieUXvyONYp5nrAri+4XBlV002Bu3DTXpLx8dDGi8kLDaf3ktkwnxXvqxt/OSD8qKRaFEiaP9bSy3ht2ebwZLwBYp1L0Elij9RmvcWc4x2VLWvlB8OhwgBSZpQVlXQk/IbkpICK7ysQxYLeTrWWlXXlGXN7xYF3zfYCL8jA0fpgpy0Q6UJ1sHeeMJ0PmchBxxah9889Me8Z/t7oEPd+QnzD/X26p+yP97n6ZeexhjL3+z8Olob3nnDT1HMFt7aCoCxWuNsTVkWWKtwiX9GWtecufWtFEXOP5ncjsAiQ6fM6XSBNt7e6I2WSLIBMk1CNpQLMQfRUlFhUaqQAiwXv0CZ5+RViclSsiylLz5Alf5Ys6En5QeaAzz+iU2g6lqDIHQW9Px9fF0IiRG2oWD8Yd8ebjZy0+FZdBd2DNIGAwtsmyH4Whqbm+c4u+cpGIK154tu38jhvuCZ+mPM64Kb3R08Dx5TQpDsHseKhcfLoc+QTh7ECp+wIoTAHnrU4yu21N59K1ZIrAtFhIE66Yf6FAjPV3QkkzYeJdn+RmLd07hcYPIOprIElSQk659kOnjWY0oa7P5bWkzZhLKsyAIF0xbkRUwl3IhEmBiD9NhfFAtu3n2IzV7AoZI4Z1jb/QxSwKTBlON58XqS85usLK+ysUHrXYfd8UtHnuPurdvAwc+KO3nzuY8yHu8zTv8SYxy3rN6CGfVIs8xjShtOSUefYMDYmueqHOtU2PMc1tQsrS6RJIrxZPoyTBUi72Cqj0zTgCkb83kbAyda+HrdJ07UtSYPmOoFTImev59beCMgmgPi68HU249qPn46ITiLHL3xMV588Y3NPnQxpmxMJgrO7ztCTdeVegy9QlC+5aKNNuSL3J+yQiHqmijx4F3nSCFFt9uTWQ3/GmiJ6I5HF0RKb11a7ZvrhLWPkIJ+2gsbma8nwdB4MQLfZdE3ujKUdR0m3O8oVaVRxkEGeb4AlKcc6fwJgWQbAlbgK2YFNgQsZeNS+8SE4GI66wX3bOzjIhvaKFIEnmv0QfE0iW1tna8jCbIw3cO6rit2dne5oHYZj8csFovG8vK9S1Jv5btWxt9XPHtvqt8beu/HWObzBYmKMSkZft/TA9o63wpZ+pRplbZNeGxHiiNSJHFPtsYHdn3/GE1ZegsmFmCWtiBVUdHZNVRHzErrduts09BdkMWJ2SqdzBchgtHcJlP4Q4NmzqKvapuft1zCxWmlr5XRUgee4skXebAac0YqQ4dEk6Tz3pghGO83Uk/+B52gfBdT1mGd9piKVJUU9NOsCcRXtQHjvLqHwPcD0Rod0oSr2ksSmRAYriqNCpthnuc0mIq4cvEZubYiH0/j0sWUCFJFiBZT1ksj2UQ1DIdz3dgfLabqijQdNrjUuiZR6QH6y8d4Ky5Mttnc3GQ8GTPJdpoW09mrwVQ2JBPeaJ3NFtRDb/S+DFPGIWTSyBmpNPOJRfiSAXkRfRgXsdcy5LKYAkEtdaPD9/ViKs7jlTDVrFMXKXL/M4mnrglr5XLjyjGU4bDZLKvKN57SlUaikWWNkFCbi9PmwpPvfmn834ZvjVkprSx5rQ1FURE3BSEk6qoXOGy8IF5RLAKnZ/njcoIvh/E9EYqyDBxim71VVQYp/UL9nfE5vnd0FUnS86nPwdpu+MEg5+0c1LpGSYIgZAzU+YXrJSM8sI3W/FGxz3Ji+VF5recemwyrqKnjcNawsjQiqxOsMXzfmXeg+5pEJBDYW2MMs9mcv2aL8y+dI88Lr1UW+OP+YECaZVTGHwaJ8p3irDXgDFII3jo7g9GOPC85ffYcLx57I1ECZWl5xYPX+UNUChEUoD1lJ4RPBU/LX6aO9FU4aONjNMZgLFhtQlMzjcOL+Pkg+8+jl3+Cfq9H4vzmUNf+NWtdww/7OoeYFx84fRE8Ihetn6CCIHymWlzmsW6CeM4JwEa59jhCENOa1xzdBTAYDhhWvoVBVdU8/VKCrjU3LJUUmaByGm0sL/Ekzt0L3U0oHhrgM3yapkMvx1S9+jCVNujz9zTfLYTgzv4y232vElGUOUdXxk2NwvPzHNP7GDoxlGUZOiNehCnt3zufT8l6Q5IkQyYJ7tBnMGmKnb0NiBuVN9LquvYp7sFbOZuIBlNVZaikJd1/AKM1xng5JbvxKNa9hWP17eAsT7lHgtXtWN/5DEdn/xXZHR5TRZEz6AsQCQrFm7duozaa+XzOmz79l5ws/oC8LrBn70NJRe/6R+kNR4i1dRbVOYQUJGlCBaAMOI2s7mV5aZl+BXlesrc35tD8LoTwGXFLh1dwwmOqXvkUmRh2MJXgFXytV3s+9Dk/J9aCdU2DLWst9fqnPY11SUzBU/0v0O/1ELivG1Mv8CRHj4U6NPxrx459mRMn7moOFc9MOB+fjOsGwfI9GSef8/TUUfs1NthywRzyG6sjTVMEEmEd04Xf9IqypJrlfiNOMp/pFa7MW1EOoSBRiQ/Ox/NGCJTyVpNKFCmgE4lpgkuOqi7ZyT4NzvJH+4f9pBnX1C045y2At608x0f3b6Yb2PcSMP7fZVmjexoZ4ioxpTbKyvjglQ2SD32s9VZaUVSkWYIKSrY+1hIfHNTGMF3M+MUTT/CeIzcyHA7Jsqy9fylIRELW71EtSvb29/iZIwv+h52a0WjEcDhqADebzTi/c85XfTsXVIKX6DNo5BZi61NjDWmiqJzxwViheHR4DOcEZmDYEUdYGw0Rytcv9IbLfvFqjS5z3+pThBiUVOBCywAhG/qgp3+FQr23KSQzlWV2/md8ira1vg894LSjMpXnffNfQJiUGljkOVobrxZMoEzxz07XuuPV+cre6HYnvuWUnxddd6gMSV3pl1noMUjcinK60PODxlN8LY0WUz7DJmJKpQnTecGyvZG0LNHTNV6YF17S5ACmnI/Fq6BE8AqYOnpojtkaAY4zCVR1yZG53+yy5CzTSVCoNZaZmAePL4qcesMj7i1KSWIJTFnVZJP7kL0BLsQHnHUHMRWMl4ip5/WC5aIMmFJBSt80xpfHlGa6mJKfOclsfdrBlGswpZKAqapif38PeeKvqG/5doYdTL1gf4OtfIvzZw3lsPSYUgnD0YjxtW9D9zI2zq/B9ktY5yiPfIY0Udjdb0QIx1EHZ6ZzVow3eFWSsrQ0REjBwNX0RstUK5/y+1UJdV1CoL9TKUl27yfKwntM4b3A9U8HTDnqqmZnZ/eKmNK6opKeSv+HwlRdmw6WgA6mwqpl8sWKu4fDZs+93LjigeKzu0SI+rd9QASQhviFVorx6uNoXSNkCijs3o1x/bNYeqyJg7QueduDOksVo/mbfRtTKZmtPAH4TAWdJ9RVDc7x4PCrfGzvFpxzfPPq8+H3RQCA4B1rLzb+0cfGtzZFf992+CQfH7+uuXZrvfSIMZpy8CRHqgebBW2MF8uL3K8/4VsKK4LExzE8eOra01gfUmd4N9cDjqLyHpOUKsinO/7D6oS62qF8qWBur6fX8+m6eb7gscOWLWdJxhnGFyEjhO+xbYy3WKrKd0EU+PhRa5l74EthEfgNJlGKJE1xWP7qpf/Ed6Y/DvhMqyjdkqQpvTSll6U45/nuKvtxxOz/wjlLAUjxi+jh+4A2cUFrg3XGS1qLaAmFZmg2NLWyvgGWMdbztfHJOJ+aIZW3kG3YYZ3x8z1Z+5deFDAcBtbYRhoE/HyEfeVlo1GlxtOl3mu+0ur+xxnGGD635lO8796cccPK2Dc5co+zOV9mMXqG3JSMp3eilAd/45kReHhBoF0taD8ZXUxZl3jMCoEcr+GkTzi5SUjyIvQOd46qGPHi+sP+d4ien2gOPcIh4QCkQIlYIKiQOw9gB7wMUxQlUiX0jDe6opQ6znHMSfYPYEo02UjX1RaNo1r9FFpImAl604dIVlZJBkP0Yoqra5RUJNc+hl5/GP2sYa+qKauC1+UV62tLfMP1t7HIFwy2/wmDzW048r+SVBkKSPsDtLYcei4hyySLaz6BWd8E8IyBdaFdguUEAmktp6XAJoL9VDK9+rM4LEW+YEcMYNrFlDuIqcOfxTrL3vo3MjpfN95aNj6OPuI9loipo5tvxVpLohQnDj/y94opIQiV7/5Z3mjewEn1DELAzbwBjeWmY/ZlmPr4qYRvOar52El/GF13/WP8dQjqf0LoGhYAACAASURBVMvy6LLr+8o95QPl4oXFfL8EZx3SutDhL0Fbg5ICFzK3jIuB3MjbtfSJC7UbKujrx5hDP4o+NhpbXlG0rCrqQBx7Lrd11VzzDXHxu2ahtu6bp3wanhmBc4aqqtFaUpcVWvgK5Ahan2kR9aaC+nGgw2L6a5SKiKC22rIILYellBRlidEalaTYxIN/PB4zGU/QWrMYrLO0tIy1hr39fbao2NvbJ0mzhq8WUpDnFVob389Fm2CRhupnrSM52GzssahJhmIeF3jp+WzixS0JBWFSIVygIISPSzlnsfg5sY3CskTLCiHC/Jv4DB1WuEaPK1o11lpcTUNNHNjQwzNrqE+iheV/1znn5beDFMcBK0h0PiCsp4tH2ymUxoK7AtX7jzZkgykvWuq7aoKwllRJslShrZeOce4iTIUJjDy4NR1MJQcxlcikwdKrwRS0cRDgAKZ8NmToPSL85kWoZRGELodVidYSWVVeGTmuE3spTIWDKsZaupiyPs3WGstCL0jTDCkOYkqG+RiPx0wmHlPX9xYsjZaxJmBqa5u9/YgpfztplpEXvvhXSw1ae22sUOBpA80U4xcNpiJNjKetdK2Zm+klMOUCpgQm3J9xjqKDKVdWuKJsPABjXDMXbXfOvz9MSSEOYMoE7CJ8yOBymIrvj90/Y5D/Uu/tjiseKGurGyHN1Fe/2zVBVRTUZYlVylMqLtYvyKYuZNL/Ah4lDqmDRRUWjjEOIXTjAjoHu73Ph14oDlsTutg5n70Qrl0KwdtWX/A329xou5nGZjLOOd6+8pxvBJQohOiRpmmTs54Pv8h0MkEgGZQDTOpjETZs5HWt/UYpYDDoUdVV45k0DysESeNCi0EzLmxSKsUHk9y/piS9LGN1ZRld11RVzXgy4WduyLn26pL/cX/BTxbP8vaTPsGg1+uTuqyJR/X7Pd71le9AKcHiBlhdWQ6BWMd0XHHnzrMICc9ccztpL2M6XTCfLhACprMZzmrqusLUFSJRyECx9AfDEGfQnguvfqmxlqbTWVN16xWWfyZMs8AYHTaBsMlJiRAWKRXaGCar/8KDwFjUQLUbenCTnXMs7/8Kxnjh78nKj5IkqqGtfHc6b4y4QOHo0Jmtm1nSeCINiMKXXDT83vDaOlZWVzeavj+jAbg1Qfn/M/dmQXYd6ZnY92eec+5SG6oAcMVWBS5Ngr1TIgG6pW5p1GqNJY3WiZkJy5IcinlwjP3gCEf4wc9+cITDDr/4wWE/2I6xJHukGY26pZZaGk2rCbDJZqubBMENqCoUgCax1F51696zZPrh///MPLcKBWoe3DgMou5y7jl5Mv/v3/JfhkPUoyHykYUZfAkYltjAvRammrqOmLr3EjOE6YtwjUPdeJgywZQDmowjKD1JYY/7YErzPuf987jmLyNgylAQZN573lPceAU2s6DHv8+Ykk1pYxwevfFZEBncmxyh1+tz0IooN3Vdyx6KYoqTh5+sLQaNx9ARyoMwVXls5H+J7cYC21/E1Q1u1fBceRYzM3eAuy/CbWxhe2sTGxMb6PX6GOwNcPnyZfx3wx3UjcM/7WTIhfaMMeh2OijLEZxz6G+cx8z0d4Lw3Fs+B5tbCY/1yLsFtrcG2N1RTG3DuwZ1XaLYOA9//PUEUz3eD3Y1dne3wAEFDp3tb2ErwdTe5gYaWmthanH2VTjnsbD+Ck7ePg8yhOvHvou6aXh/0xOca2CNFWEuipMKfOcCpggEyrJgyTdiAQKMm0V6R6xaj8qVWKIrgOzFzPtzWDZXABDq6tMSeBQF6U/1++G+9zseWBySxI/a62Xo9yewt7uL7a1NbMOH0NCgbUgOirrF1M9d100i1dTTDZBnV4UWW8szC1ftCbHzNZwwF80ZAYSn6rXUGkm0H5JoD+c8hnslXu5ewT1zDR3bRWE6bDk0NUZ7e/CogEb6bOrF5QjuBon1bqRPdpOUo2a55rC3t4ftIZcHH03K2CqPqiyxurYKgkW328XecIi6qrB6dxX/tVnHYHcPLpsEjMXO7i6+Ue1wIURr8DUzid9/9BtomhpfuftbqKoSvf4E+hMTMHmBK0fPwhiPjvWweY4sK2AzrlpgLUCWY/qLopAZ9yBrQTkX3BsNdjHq1nD0G3DkUfkaTV+qEYRy19HKVC1Ftanpzf9dlrPC1vR/BjMsQSQlUcT/GqaTPSisWU79rqyxtH1OhEGISLkPzaafe3V/RYJ46I/M5nhxnYsiUa+Pfr+PvcEAV1Y8gLVW9QfGVZIoDA4Wqecuoa4a+Ebpn90egGDKOViwAtEpikMxpbkK10L2djT/U0xpBFXjPOphCT8cBrdk0engR0UG19Qwe3vwk5P3xdRpb7DXcP/1GvsxNb96Ifjyl47+NbDN+S0/urPBT0keOzs7WF1dBU19B1m3QTF8DnVVY/XuKr699i1c//gmfkNodGdih0OprUEzdwlN42HufAGuqbF69y729m5HTMlcGeNhH3sT6M8i2/ssrK2wN/ltFCCQNciRwzz6Jki6JbYxNQA5jcLikN7GRevMzb0h/ZOYSWdrF/Bkk1goji2ZJ259ActHteum9EIixRSFeVVM8XrFnB9KJj1iKuXBEGESMbVE77DlScCpM2/jxtpLODstP1k//8no+7Avt7c2g/8aYLOrHJUohyNUhcNgOMTuYIiyrLlvCBku8awHxaiBw/zZKWDiA8uPXRr2JrxD/Lt8D/mO9J/oIXHw8JKwWJZsaVRVjUpKQcB77NbbKEdDOIkS0/GkVolzDtDICvHvk0ka3XiOKiorvm/dkY5yABrjsbm1hSLrgMgiy3LslRVqv8vuQyncVlfc+a5uKnhv4WFRu2gdjcoRyqqCyXLYvEDtGhhIBrP0OdA5ck4tQWU4WmXUhKzpRtxho7IMexZ13YR1g2trIuzrThi/MDyI1cZaEFssrPA52XuLy+TFuuJbkC5+YIYHUMb9iWbfb/S9T2jj4XN7bW9vYHt7JO9YQ61GJYZ7BahxGOwJpqoEUyldkxqpbCkeeHhdH9f+8ABMpXsz3us9oi+FknurJah5EPfF1PY2+sPRfkxJVre6wlA3h2KKrSn+fVU3YWxVXTOm8hRTJWrPTNpYi7qqAqasMQFTTdOAZF+nrkoMy1IwlcO3MIUxTHnpx6TEdX9MOQmeaWOKTUWnzEmuaRL/fcAU7ocpE4RIWKYDMaVX+PseY2gJfNYfdlbrOFSg3LnzkRRjIzS1w9bmJggGRVbAH3XY3N7D1s4u9oYjbpplM8Cwq4Mo6EyJsNAWwMoA1Y/K2eC1iS2CA3EDgeERqUqaaKehjncyBzoJAgrO86hk43C7tYH5f20vsz8aHhNbfwT0e+z7FP+ya7ww0zoImKAVJsD89QGhdhxmWdecwEnEi765sYVO0UFedPDffPR/o7PWh5bD7hUdfPeR38JoOEJZlihdBesdjHMYOgp1rxrv0HhgVFXwA26M1c2QlGenMG5OFG1gjPSXbhjYGhvROIfaOewUNZpKXEo4SJNpv46fMXA3pn4nfNbWiCDMCEhoHPDAxvTvhHI4YQ1TCZAIhvFbR2skRrCM03v6m4dNmACMqbsbvC+1tDqJq3P3QDA4sTGBfmGwuXMwpoCIqWzjPEbdb7d85S1MwcOuvQTKDGrbHIopjeoLnwUh0hbM3gPVzEUAgNFafvfB1HpVY8JpLepIU8451K5phbiqgDkIU03dyAY0MZ1KJ9dy+hI2NzJMDH4aedFBXmTYGYxApgIB6Ha7GI5GGI1GcFsvojn2XVjHmKrLErlgajRzEU2VYKr/N4yp3IBj5MQydw5m7SdRzr7K1gsRgAbZxnnG8eNvBUzVjZNIVfXWIDw/4JLXwPzahWQtIPthkVbm117B9WOXwjnza+dBRFg6Kp9Bl8knaxhfh8ULZ2vEXrxJKJ0TsMjCcd49jxvJLxN2euhxqEDpdLuwWQYig7rmPBTe4fKY6OTYzC3yLGO/aF0HbSpNsnGVaCU6K1BJL65a2RDL80wYkWbQepHG4Z0AQivdRsGh4XJxkhAiHNT1Ffrbu7i5BAAbw2EoD9OX6csyg063YAvDcZQVSUht0Mo9JRvBmrzJE/4PpidDJ0ojGzKPf7yI3GbY29rB7ib31LZZBjcxga9PbaLyNUaetbxyUKJuGvxxlqMvYcR1XYM8l90eDkdoyhJHZvrw6MAbg8I51HWDuipRVQ2MYbfXl4/+s1BjDLKx6nyssXRYXR6gnc2vLkB9nX6uAjT97Tj18frdz2y4P6kqoVPgivcfs5bmSS3rh+nodHu4W82CyKDbrzG47OGbGn949iT+8+YO6PgboO4A+XQJX0cLpTbrADhayqy9BBq8HCxxgscpZ8RNdBEWEVMr3uOxB2Aq6LM+wZhUfwgHaevfNqbYbRYxddIR3GiE3cyE5D7FlDGKKS/7A7QPU9dmXw2aBvnoGj995i3BlMGNG1/A2ZlJ+ILgGxcxJbykf+Jt1JMNqFcDVQlXszVVNw3yLMeTezWPffcngsBy8Lg69Tc4Mj0BjwLeWBTOYTj1HQybLZR5DVMzpkwm1T7mXkdjLCy6XDW7hamDaFRUbJmW5aOcKX9m9WXA7cfU8rHXwmfzaxewdPTSmLAQAQJzKKnPr50HPLA4dzGMQy8TMdX+zRK9g3l3QR4jwdQD9iQPz5QPESKAdjxUVmwkNDPU6BGmoozESFmI4D8RYZH6//YfqWacfNLSmEV7GvcRChHqqSkTconkT3u86yZ7KIoIYDDzq+gUFt0iQy69F5racXMnH4FIkkWugPzDjsNv7lnRNDwu3fo/UWQ5vvLM7+HEnRWgP8Gm8O4AJpeSMk2J4WiE/+jmN2G7HaAo8OjJkyjrCmVZwtYWf2wt6iZupMJ7/Gm5DVeX+M2qAFkLk+XIgmbXJlrvgX872sEvFlPQCmpEptXT5X6HzqH+hsTa894DsvEn+uc+Qo/zj7E1aa9vev7+z1KBJkSNQEqJiya1dig5HzgY2D/GgyK9ERk8PTcNVzf41PoK7JFegiktyBgFY7H5Crx3qMGJqCmmblg+f2HtAm5lBHDDUpx0QCW3PghTHHraxhQzcVlnolaBVeUJJxqPxdlL8PA40zwPrM/E68WT9W6w1qBTsALq/Z5gyu7HlP7CiaIGBExBFNBet4d128XZfg+urvH42i5udjgC8mRlcGttDUW/j363g/7kBKqyRFmWuHr1U/iZM0BpbQhB1xt45zC/eh79PS7m+vGJH+DW0qdxZOYiAML82nmsHP92mEcnkWMRU/QJMRXpM0TfGSN9hrLW+oQOtjITC2sXACIsH710oGI37k04GFPjOInIIgLOOC57j/UZrBiPUy4VIglTuc9xqEAxxkY/gwCAe5BIZd6mCeGBOkYNz+WCkCSljhOJGCbJx/eJtFRfbnh6PVNNLmJNZnztdBHaTDAMSpoKEbvNhIQT8dYyGQEKGfJOTF6r38nYNMJCPzLKKAxbHkVWoOgU6Pf6mJyaQjUaYVg3qIclLLQ0v8dw5OF9jTzro5icQH9iEnk+Qp5xLkFe5kCpNZHYKrKW4B1JpQCH2iFYgWz+IriwYkZtwtj3z1xytM1lkjU11gRzOnWhsFBN/N+JSX0wvtK1uT8AUgtT33MrZYRVa18/FWq6lofS/o/liJgCQKaFqTpgStxUijskjAckjJeCEdjClNDgJ8UUyxMfs+7jleRFMufhFMGUYJy0oKdiiqIwTPdHuexKiqnsvphSFwwRb4Rzc78MnaJAv9/H5OQkOp2MMTUawRlW0hoHDLe3kReFYGoS5WiEPM/R70+g222AgjFVOQeDILsYUw6onUctiZY+8IoxTHmOfDsYU4cf6p5kTInyTfEeiimtI+hVc6LD7zOu1CXLrSw8UdJE2QuYGr8GsaVK+iUFHnwYpB5YeqWpee/AGMvlUfY4DHVvYws7gxHKqsbEzmd5wEY2vobr+AsMwYXfSlSjOiEqKxol+0s73QLZyCKzFtbYENLH+wENd4eTBjARE22mp8dXZq+xpmoQcloAzgjXcitVleyFEJLEOT6c+EDryqBpao5sasDFBl2i5Xk23bXnvRNB0ul0kc/N4nfKr6FTdNDdvguf57j6zmVsb27i2boEDcooyrzHZ49fxczcHC7TF3H31k3Zu2nw2KlT+Kf5l1HmJe50OmIlEH535zr+j+4xKZtiZaG5Iih8DWMkox8Om299jC91u9ikEchYvPbiEfxKloO8JDMFxTQmU6VH1KIS4oYqF/oIKmA0Ki7OUTyi5qr30H4y7ful52p0Ef/OUxRaqZK132/cHv/DdPT6faw9xaHqR69lqCouOdRUJU7PrGJnMOQaW0BgAoYIna1X2C2s/6WM2hicVFczeSzYHBksMseFWa8d/1s453Bm9TyuH7uIsmxC7pVO9cL6hWSUPGeLcxexsMbRPdePfxcL66+IFk6AAZ7Z/mnG1OQqnLqdE0GmB2OqQV2ZpPMk2MgSTK0Yj5MNhX0VFZR5nqMounhh4vfwvbuLaOoCc3M9nDz1Q2xvb2MHW7D1AmZ2KhABWx5YeuQF/Fw1iZlmFtPZI3h9LkfjHF7ofRcfVyUw8yLKMsfcdrQSFucucR6ZNciyDM+5L+AugOvLL2B7cxPu5Ku89wMHZxrYrMt9QxxxfbPGgVShjeZygqloral1yphSHLIikAqUs+uvRCEm3NwTcGaV12QpuLDaia26NxO/5Iue3XiF1zK40ghn1l6OnprZTWivMn90C2ZjGjflEicdHbi248ehAsVShm6vAyMNWd77aBnrwzXsDQawg2No3By8B4bYYwbpeXGaKYufQV8YEFe8ZAbA7ie1ZNhyiO1LjbFo6l7QCFQTYGESTJRES2YzUzPx/3p7huPiYfFz05OwxuBvdob46uxUCL/jviSct1LXNX6lt44/v7fBbiVojgtrLU3t0dScoVqaips6acKS5NVACP+3BnfRmf0Uet0eHtm9BzM9DQ+P5Q/eBRmDn5xbxna+jZ2tXijOB3AUSAmPvaqENGAI2ujtGysxM79cweNn5nlhJibwe+UmsnIHpjbAEPDrhJmqQr8sUdcVVqcX4KSQXQin9h4/9dYO/Ben4Osm9XIgErtJBAj70k0y5zH5yrdcfqx5A/C835Oa9Mr8dGOWf/P30uniK4qKgtI1XzspVQ0OigAOjy78cRyWMrwy9DAmgz9NaO7dwfruOj64k+HePYPRzHYitBHoIctzAT4hsznyXicIWucdfjT7WhCyhgwW1i7Ae+A6EfKtn4L3HjcefQ2568Fm+zF1Z+ptnFk9HzFFhJmZaawduQJjDGbNHI7QrLT+NrDHtxm7hpDbE6DpI3Cy9vfW1lGVlQSDpJiiMUzVWHR1oKlrR17lch+eFb1Op4tO0UW318OxY8fwD48fZ0zRv4TzFqfuvYzt7R3s9DeDwgjvQX2DSxMdTE5l+AXv8LnVp+Gcw3XzBvKiA/f4D1A0DWY/elGKwQJTk5Mo8lwKPhIuXe/DYYDMeExOdnFu76fx7sS/g7CvFqbgGviKBFNRmOjRFiDKt9SFTMJfE0wB4vIaw1RirXrvpSJzdJEC2C9M0qOFBR6jbvADwLw/18JU9PCQIEswdQh9HypQ1tfWkOexmGJZDuEcb2jVjbp42mYwAaxJafibN63yGS6Y8ogaMsQcgwHl/EXa91qltC6Ozk5kfrxYk55CaYjpqUlYYzFjcxyZmZJzuPeKFqTcGw2xub3DAqlmLR8J82ucRqNoDS8EZpjnFsiZMPI8x4QZIO910SkKWDtEXdVwrkFd18ikwmteFJxUaI2wPY/MeeQF74XUdR1NUiIAXDvJAqCig6LocGVd55C5jItHSsiw8x5FniEzhLrJ0O10UDc1sjyHNTYy9UbySRoHr/0NEqsrRnoJUbWEjlKlmOeUrFuSs6KBCABbiuoa0KRF3ccKm/x+nEwPFiCpANTNd8avCpJorhvSkOWHS6Ksr61jfbsKLtWyHHHJdG9Qa9qGMfsxlal7yMMjVsFWTNmiQBBCXnLICLCh3wnBFp2WcI82I//T63ZamJqQ4rDGGmTGYrqZRGa4rp2dkRp/hpN3TTaLuq6xNxphc3uH6879PTGVZRmyjCQnLUen0+VyJh3GT13V7Bo0DbLcREz1OWqSxPPR7RPyTgdkjXQy1CrmSpMWFnyObbiyMLchz2Rd+JnhvPA/g16vGzDlvd+PKQimdMES612FRcDUPg1/DFMQxe4gTJFYPeDkTw2qUVqwxgRFoYWpcOG22ys1980YptL9O1U0PCA+woOPQwXKrZu3ZOG40GI12gMRTzKFvhnaKy5OUmazxB/qIvETwHHVYpWApCwDpXwNlGjVWupDFKkWbwuSX8zUfzQ9JQSfMdFbg18/egTWspCxmUWv04ExhFFZwWwTl144wITTGjpaHsGY6GIwxuA/3buHzGbIsgydrINsagoGt2EbC9dM4OqVtyVb3sJkHq9vnuV2qMNheAYvz5C7HFmdodORUvc6H57nSKNbPr65wu64usZjJ09BbWCyhEwiuOA53v/YzjLqqsKRMw3++JEFSZbj4In/uJZeCdpPK9FKNIxU3VFEY6GPLN1b7kdl7koPaQM1fQ8vlaWlVrq6JIPSoBqazo0HtPmTro/6unkNSDZG+cPxfTWyJEl+D5dAuXXzJm5uj2Ctwa2taazeyzEqp5BnDRa39/Dl458BgbAojY0AsDB45M0kbN0h33oFzdxrAAgWhMx0hBExpoo853mbu8j9ToxBTh0A7DUImELE1MdPfB+6D2iMwUQ2Idhhy3599m1+n2V42n6ambC1uDs6DjpGKMsSRzb3Y2rp6EUUeYZuJ0e3m2N77dMYeofcO1RHJBSZDLrdHrKMrYROUSCT1grWWCzTv8SgHiSYyrHy6PdQVRU+fO8ZnJ3kvvEewMK9CeR5jizPsHf8WeRZ7Eaq+71kDLJHduFXp1nx2ryAk6MKy3OXAPJ4qzmBz+cf4dTptwB43HYevUEfdV2xkpjlAVMcuCOY4gU7AFO895utn4c/9jo82DW1OHfxPxhTnBDuBVP8+cojr+PM6sv7MUXA4hwnShqKCotLmKohwhsSGAIiuNkajy5x+PRN4+Hm2Jo5ffel+9L34Q227nzMNzIGnSLDsGg4s1X7HGkt/UCZbL4VnZ7Ep1fiJml0KmCMR7/XCcSyubERJsNaG2rHeLBvmPmBC8JEtU6SDnH6eWYtsswiswZ5niHPuMGNEfeNtRZFlqEocsB7jFAFIohSGlBO7lyNwZA351SQZ3mGbqeL3x7eQ//YMWRZHrL8UwC9//YP0P/aWRARRt9agfdcqTkvCvR63ZDZCs/10YxoRc57yXLncg6AaHB6Ye95fo3F2u3bXO5be8iffVo0QXYxFnnODci8x++MVqF9HhrnUG//CHVdo1dVuH3iKbQXMVopABINSIaQaFr8E84PYm3KtIhTljz8JSJYxJpQ/Mwchh5PjmvhXbRC2SWKoLHz0UDs3aCEMB1JW2QgKUz6cBy3b3+MW+tDGEO4MxjFCgwyp1zkEmjQCKYMjLHoj74cSspXR17lDfyK3SDGWPSlv4exBpsbm7h65Ds8F2RDeLsnwZQDICnIQZHz7bkmAuNJMZVlyAWzypQyy1FbZ6f2UDcvYLA3xNbq2wFT0QPBL1xTY7DXxlR350vodrro9XroZ33g+PdgjUWx9QqaidcCHnd3d2U8mewXKqZyfOHMkxFT8BGT4q79nz68B2stfv1TbPWfbp5DCKGe24T1Hk+tzQBZF88OfgauafCLC3+GGyufgVZPJiL2AFgL3/HIsoyv4xpc9W9zEioA65xYCIypbP086tlLwTXsvYdZe5mDE7zHmXvn4TVjRzEFDzKaW3QIphD5Jn9MWFh/Bc4cjKlntr+ExdmLQWAREYwEVBABK3gPxzZZQK7PvshCVxRcawlui/d0bPYfaKF08jzkUnSKAiM7AsGhgQtRP2rOkiCeTU8PQx6ZJXhE94ZGrXjXwDUEgAkWQthEEqJIBK6bqVFZylBiNIv6EkmERZaxJqXWiJYNiaamZL6LBm6JuzNayyaia5wUe+MeB3UtbTQbHsd/svsRJiYmUdRD5L0+cm1UFFxuBkvvv4u6rjHxtbMo/2olGrJRXQkLr/qImsQhv4a4yZcvfTDB1VWgc8hMXSrxGgNvM3y8cj3k2Dx5Zh4a7aXWoDUG3hCMmOtZlqHJcpy8uxJyAe6cfFr2hdJN30SAID5GeKsAcBRKQ3gHKaNuYCgKpfDM8g9HA6U6coIBiDCgJBJPQtX1WokSF2nBmNDtzzs3fuUf+9EpCvQ6bLX3mg6O91ZRNw7/vXsO/yS/hZWMNXbr1ao3KLZeAXU5kjCzBG8tcOx15MiC6yJgilgAAIIpI5jCAZhCkhjrfYvRsPVhkIlbK7M25KEoAXhJGfjebAdzG7+PYT7CxvFNrOzexNb2NkbDEmfp03xnBxaeVYPR1KsAgKLXxUR/EkVRIM8L5IOfht3+EogI7ujrsMaiqtgi16hHVTezjfNsoXlCPfsa7OpLwdVTzVxkK4YIN3ARv/wU00xZUoIpxpnZOMIkRVpYlbH87M6X8cysh9v8Ere6kAg8ORlZJikC8MhnuVZgZjPQ9pfQuBrl9EVW7ooC3uZQTJ30BNPw2ji4wO9Fv4x6nZPIyZAMCVhqY2p+9Xzi3uIVXTp2EWfWzkNBtHT0IhLfDwwibbDgiv6SxPuFuY03sTb7xRamTkhC6mE2/6ECpcgzESisiWRUwxsvm73MLDmzlhkXjG7mOmaQxLWHkBAqlLk30rbSxDIlALisSjh80KJ0ozialNRi5lo517TOi75E70zCJOXaJO1SKbrvAEQAEiGzYqGhg35/IlglqWXCpnQUWNw3XvYHBIDhrsqNE2HM34uWL9E6jWvE1cgaQiNF81Szc441fJkOZp6OQI1joku4vpc6SUTsWrDGIvMZGyyHkgAAIABJREFUmqyBc7lkLjfoFF0WZs6FvRok41ZN1o9RlGfPWzTdRQCR9xyZNebTpWTBmYCj7dkihvTj5G3YTEZQYNtalzFg6wX7xvrjPorcCq7Yks6kgjcp/ZpYAoWVNMVUEzBlDUUhqpTuHBxxV1OrAkXuGTClTAQJplRIQDZ8xfoI1n2KKRUmAGvV3gSXpdIzCyPBFCWY4oGAkGCq20G/34+YGkVMeULs+ugcTJ4hVNuV50i1fqWM8N5LELOhkFPTOK0byPPHGKJgRPmE4QKxtbAnSShNMOUEU4Y4ctVK4zvqduBcDtNlTwQVnF+mrq/MRpYbQ6QVW/oF4yk8698LU76Fqf3u/DaoYtCM3JggCaWJ0k5JZGYkgQOPw/NQSIjLxExbndMmTRZEfFjnPEbaLU8ZDSRPQwfkuJ8IarTCVNNN4NB3PpS91gcW/yGZsHkZNFcfQaRP79T0tjwgJRItUZJnOYq84HacxHEM1hh0sgy/Xa7BEUefdY7McXc6a6WTIrUW68PLb6H71Xl0rUX1Vyuyh4SQn0HxIQMRQIGDBOBWhS5HdljDIMtM3PDQ23JjMAGQsbA+A3Jg9eOPQk2huq5QjkY4cfYpEBHyvAhmbHTv8XXOrN0KbUWdc2jqCrdPPC0bqDWHkAeyCsMRxhaJzXuPumoAamAMYhMoIXCySeSYzomPrhalhfad2iXqw81b1B0ZSis68CE6DBm8NfsyjCE8MvgwgPk311bw4fYA881LiDYbz4mjuoUp5zwgXUXVZeW942KRGMOUizliRgt3Igow0QZF0Uj2N5O5HedJznuUzTl4ygCT49N3ChAtYFiWWHf/K/Isw7P55zFsOAXg7NoF5JlFJ8/w8cm34MD4nhh+BdkeY8of/W6M1JJjb28AY4j3bMnA+VowFb0j/JBRUTzpDZb2YcqGc07d+Qng0SGMsciMZz+q3DMnhHBqzgOxUBbpXCEdJWupHD6SuSHMr57HjUffAJHBnwyeAODxi49PwDkPO5pGZ3qG937WXsLaVMVth12DJyoXMZUwU1UmdAm8rGPtEkzRwZgyhpJ+QBT5o94i6mJYm30Rs+vfC8Bdm3sRs2tvAASszbwYlfK/B6YOFShV3cCD+3HkeYYm085+USKqtsMauUjdJjJQ1R4bBzRQS8MAjmtTpWXJjQoJ4uu4pgLIsIYlZZSVmXMEm0SANZygBCuRERTHBLDwM87D+rTfthfNEPhn27cwHFX419bin7stdKlAx+ToHTvKEVpZJm09EyvHs0VRlRUu/92bEu3FG4n42dNo/nKZ6UOFj84HATHbX2hI51HmR60+57hsNREw8QvsRlNmD0iOhkewJlhr5Gtay9F5WVEgLzq499FHcE2Nqqzw5MICOJjBoijYdZcRtcphe+fgsgwn76xEIdPUcn/ugc3+fofV08+m9gXrSBTDDnmJE+0RxO43JXTHGi8c4vohtmhW5pr2uTYUS4NoNIyVjcq2pfJwHdydjxtnjaoaf/Ujg6bxaNwuCIRGwn+Vseq8Zusvo5l9jT/z7GJs4CKmSGruATjjnoO6Tq6bdyOm4LlgI3FOj0ojSwZkA4UKjgVTaO+r6Gp8f+5DPLrzQxQmx3Pmd0LFjDPN8+jiHnbtAFmWoa4bHJkHuh2LTpHh5MSXBFM5bu3Mopl9XTwIE0D/ByBjUFUVBpuDBFMZTnz8k4GRLR17DW7udYRYSSK4o98FAFxHFLSKqbN4AZ48FukyaG4LjZOk4TwDHh2EyDO/NhM2v6/NvgrvHJdF8ew6ivKL82pO3/1JNHWNqqrw6I3PgMjgq0/+EYqiIy5Dwr/eeQT/5bHPw3uHNz5FcC7DsasdOO+x5hyerJoDMYWEbRPUykzrIyJIiBRTp1dfxuLcq2FvphG3r3oo0mKgc+vfC2VivAfm1r4XnvHo5vexsHaeg5vIgjxhRO6BmDpUoNSuEUPYSDfDqAmkYYftI9EsvWoKbSskxjhHYRLN8DCFrUt6YeDwLK1hYpQKCKGSqfZXVsvVee3F0qCugL0h/340KlFVNfIsR6/XQ150cMRPY2piCkXBESIctih7JVKgT91BCL0inGhROYfoWgtTN62EvNRsiro8JZ+1j3AGIUT21HUNeC95ABZlVYZ55n2VYCOMKfHcMpWM4R42ZEJOA7xDVZZo1G0oLWUtWXhDIGdgXNTYnLUI7ZKzmrs3ugZZViBEDolrIFEew7j0cM7BwMTucnJK1IqJ+8l7H8BEYyb+QVFKZCT7OIns2mfx/5iP2jlkY1ayS7DkW4uX/rKNPTKRrrx4AkjfJD9PrR2MIZbnl38Y3UTxti1MJdEhTix/DosHhn4I5xxGo0owlaHXZ0w55zE1xTkeeZ6h6CqmMuSjApRxLyFHlGCqSTAlylxSUdvEXAUAtA8/cerCrMZPCeCqyIzdLMs48dlYVH6MiSfz1MaUgc0ggUWMGe0VBHjuQdRIvS/hD95rcrWR/Rdez4KyAzHlk/qD3nsYfHJMucCjUuJgi0X3u2P4cnJdQVfqKWBMqVKoz3/AhMvxAIHi2UfoHFxDaIyGGyY+PQgjoCj5hEJ5kGKet0Jh9RwihAA24oWKvkDZI1BNzfng32S/rRTTkd87J5tsNYXyDgATPxH3X/Cem+M4D1Rljamrb6PvPTA5CRDht+mr6Ha6IfLKZllgsmo1qZZAAJbefxdVXUs2bwH3NzfhyaAejcTPTWG8shSt+Y1Em/SRofQ7kn4VHttf/wC9Xg9FUSCzFqPRiH+byqeQeR6ZT9ys5s7SeZ7j3kc/kmoAJUajEU6efYZ9+kWOPC/AbjUDCw9vjJjPysSVyAvUNVcSePzjJTRivXjncPfkMyEBtD0WhOtwlJEJ+zRB6VCfe7BeDc/OGBF7+H2MRGuUNYldf3/j/Mdz1A3vL1oAH65tx30BRBcm01fQxkAAqiOvQTW06B2JbkPVXeZxLsCC90TSCESKrk7wOhhxnWiIf0gugiT3ugamEUxRxJT3HnenPw9DwOOrO7hu/gBVVeOx8ikURYGi6EBdad1ulzfJjREBwYy1mX0tWplg2q/qCq5pWPgUMfDl+rHX9BEkr+nglW0JAnlGr7wJ3APkzL2XuVshANvrIcsZU6XfSYSIzj3zo/nV8+HKS0cvgYzB9UdeB0CYv/cyrh75WzR1BVMZPPHRF0FzWzDG4F+cugaPAkSEn9jg3vbfe7oHwOOLHxbwecSUSzDF/0dMtdzbPrqdEjUScIypU3degiPe4zmzej6E6C8fvdjiC/p6fi1m3iutzPvngblN0O4xGDIcdfgJMHWoQJm6975Icib6abf/Ypo+FhJxKGoSvGlFrda+NlOXBE+GkYKSYXM70UQ5UUgX2EuyI7/WxDDdRNT4+EwIUCWwbvrzgjnpT0DoEIHyHIXNAuisdg/UjUG1DGQWnecoD/LAjWsfIPuZ07AAzF/dQFmW0lsagSEr1CPt++gyQ2QCXkFsPIxDZKqQyDbvAQJ6P78AAmH4F0vQul5Bo4BkQAqphfwOF9urKjOxWQ6b5Sg6PXS7NTbu3kFT15h99DFsSylytdxslsFIjkuqGWnYJrd3liQ1acDVvXszVKB1TYPG1bh74pmWdSqP1NK6AYMs0T71fh4ItRAD3RESrTXZE4C2S9XyOg+XiWKMRePfQu3ZSm5U85NJOd08HzGV7NUFTKmfPMFVdPMJGUFDQQnP0ueD5eG9h4Pjrn0+KnfcN4iT8sI1Qr6CAWBQ12w56Kb77OobbFk4j3ea70AXbaXzIZ4y7P4xhvcvls27Ml5xGzSAmqUh2VU18bWXUQAoyGBA3x7DVFzvdtRhiqnE4iLCAp3jfVgi2I2XYSF8RISMFat7VNchlNcDWJBS8bBMgdbH+yysXeDyMHObYVxP288CFljsvIO1+fdwxn2K22/v7qC+a1qY+skNdp8jj/OgCmWR5+KSb2OqkRpvKaY0OjNa8ipgvODFAxQxNb96AXXdYPnYpcCT9ltfTAPLuIIFnMP1o5d4ju68iEc/AaYOr+XV7SaEympPDP/liSABQ6y0mUR2EIXN9dSv3Yr8QGKxpB8mTJgfPJkAOS0KJhKDRyOuouTW+PDooqOoeYgFQDJJITSX2hvukcOxduk9sLs3Ar7+IbLMckLWz57C7p9f4z0PANyAR03N9Dl8mzMG9VJqYOm9iaLmKD/Y+JP3wlpqdBsRofPVeex9c6kFMl0XtgITLcZrDg84gshmMOD8lsFne6iqCk1dY/DnH+DEwlkOQrAMhjzPobkKLMI8vOHCgkQEL2vtvA8tYL2A4ck7K/w6lIng/5u6wb1Tz8ocyR6QYxeeD5YXkn2YhCpk3Yg8DKy4Czgh9WGzTPTo9brY/WEFgPBLL2SBFr99I8Pk5wosC6ZeXB8GGlyiK4EmdL/Ew+OsP8fv00TCgPdoaSglqyA5gmk5NWIrYlQtH/5lxFR0ldS6cQ1VBJQ38H1USDiJOixNGQVKsjDZxnnUM68GJjgqK2SjEpm16PZ4P/Lk7Z9A3TRYPhb3j0BtC6W1QS9zoA/tfMT0v1qdwa/NbcRcNw+8NxyAAJzyPK//89asCGqD/2J6fT9eQVg++lr01ADAHAJf5L1JwJqchal9Dye6T6OpS9R3LXZ2dvZhymj+D1i4m0+IqUasl3FM6cY5Q0oqYiSYSnmpns+neug2BBELqLqR9iOfEFOHhw0XRdCG0hBZEwRK1LKVkUeQR+JP63eFuGd9sJRvp+9VUGDsnOS0aAeNW07sEFHXXNp5MCRvyayx1uahIY1hnEThmvoevgkWVCPhuRFP0Y2gwiBpqRasLtVI+N+oigfN636L4T3XG6OY4GSlDEa308FItatwAdl3CMLJxTkHb2SSR6h4YGGQi7uvKkuUmVQ7qGs4R8ESCYU2TZJfRAC8ZeEJD+MBb4yMx8E0jhO+nJOw5OgfJhCsdK30ogkEBcD7sM2WCmU9mKw8A8Z4QIi/SZo00X1cIz+uoygKVAVr792uC9Fu3GK7AMDk1B/JKw90qRMEj1oozjkUPo8KiB5jj9u26kyoco1wJx/OC3YQYT+mvLIrsXR0n0qwlCph0dUpSpsUQKQ0MpQIRjRwHUHTcPCMt8nwRNlKFYiEI8bPwzD1O+xDVLRkKOBQOyrW3iCTQtCZtcjzIgjQZALRqsAr/C5Y2aw/SqgyW3eF7aBruqjKEsgylGXZwpQXTKlLUOeGCCBvxZISPhaEy4MxpS7ngzAV1yfOi06jFub0BDiShn2AdPmUaTgEU4cKlDzPknjyhNuTuqoik4xNtCCRSbrv0LRbxcpgNHKIwoPy1fX7lH8QIeR9BBeaMQEALYJuEbUQbrCq9gMmCEK9kUysFy1LGTKBRCvwuHHtKpfD/tpZkDEY/eUyqm9cRauX8z4LR+YvCJnklAPWJ17Hx7FTtDLqukaR5+gWHcweOYIdYyTSK7m9CDaeY3ad6SYrCLLZrYyAUP31CuB5U31qZgY762soRyNUZYlHTpzEzu4OMptJKfB+ECxEJroElFDFMoL3sDYSsvqD1cSHB3r3boaNRK3w3DQ17jz5dLKOaUJWnCUPAI3nxD55Do2CU7n+MB15luHnztbRihDN/iunG9C6tgZWZsxzadZflooC7Lp4cnqLA0uk00mjiaPeh6ie1JJ3Ms9HcBIAN09iQR7zTRbwApbtexFLUNI3YSJbmErKKUUdkNfjOr2XMHLGadj3BLAAtqyWZy5yaSPvcGb1Arusi4wtsGGN94sqJBbLjcdmU3CVCJmIKbXu3hHeYPCPzr6LBsBycgWdpyVRfv7F8d/FRL+PudkV3L69jcXmh4kbjPuRqKtVLaMzzXM8TgKWzBUs+BcCbs/SC/A5Jyl3z3g0jcV75fdRVSWe3HsKO3f7+zBlBFOLcxcDb5yXwp06ZsUUfNKuXDC1NHcRp+++vA9T12b/Fo3UWNN1BIDFuVeV7MAdXphertLbOFX9EwCEW80SW5w4HFOHtwC+u9ZaQyNmayxeF6WeMkDVGNJw2Rh0IxV6W6Z4FFJhsKLNBw3BS10nE03DWFbF4PbyEqyV2jbWJhWMowXgeQbHjR0kQ8OTZ58K97tx7UOJQ3dBEKrZXnQKNI3D9jc+RNQCInOIVx6XGON/x+YsMMBUEzLhLGUMACGzFlNSAPNHv/8mhqOhXDIKCH6buCsAznMhCtpMIwlaUMFLkVFY4r2motvB3vYm7JdPoPtOicFggN3Bbsi473Q66HQ7IWdGLT8vll/Q4jxbRhyaqQKGI5aM5+gs9Wk75/DknRUx80VgOJe4xyJzk8duKz1AcOU8TMedu2u4vb4HAPj3Kxm+ckaUrxRT8CHZEQDubjQtTE24LcSmgLoPkTL3OAepQr+Id7CAcxiA728kXJhA2MIutu1OyDvLsyyEYWvWeYxcRMRU624+CBUxLHDGP9eyJJbte1jCO2LlcNl8QwZUGLjGSZ96zzWuGu7g2HKJt45xPI19o7ImGdu4EHRiZQBcg3DjyNexZSz8vXmMRkPUJFV+yYTKvITEWAJhOXsv7tl54Kp/GwvuXMtL40UhNET4FH0R6HiYKUIz12BwKzsQU9UUl2HSHKHgqpvdAnmPa3hHsOUwf4+z5p33eGrjS3CZ24epbje2I9EIVR136jLTNSYi/I+GSwD947jsh2LqgYmNQdNNlzRYlVEroJSUE5cXob2AJiGOdK+Ff8sn6sLEVAMxAU3UqDUfxYjbJ96Pn1r9iho1E4VJ9A9HWSb3VZQShU1+L60966ZBr9/nBCOJwvA+WmhRkOwXWjpbSojhk+Av239uZA3RHE0P5z1GoxGs1Osqijzk1oQEwHR+k/eGAC85NVA3hGfXX8ixFoFspE6QIY4Q63Q5QqcsRyEJbDQacYE9azkcVMMpBbxaOVoFS7RkfGjulL42ZOCMl79W/MYMikwCAdKQyjiX7blLBc/DcpiQfxM311MSOAhTada5MqhAvj7BFEVabv2V6xbI+H8S11pyzRwZCpsHiz6znwRTCe4jF4J8FRSLwMylzp6XDee6qdEpOCdDI5uYFEVJpXClA1Diw+dtTLW/R+vXTBupMpweiiljmebyokCGGHiS6ohEQPBxyRqF7HYtpSV07pCsByW1t4gtwG63eyCmdsUjYK1FXVfQ5mxBqQ6WowlKGh2CqcxmEgEbBQmXa1JcOvk/zmXcZ/PqNd/PjJLjUIFy9+b1hFDidXRyUhUlZaMsg1Rjb4c1xvMELAqqFsmIJp4KpIQ5BZNb7sOFFOUunqOxyoob+TTOtarWBsuF2j5pIsLK1Q/CtW3GxN+4hmO7ywpT//Bp1HWNzT/9QKokm7bAHXuEfYcfe+kTbSABzjh4AsiSz5x32BkMkElyYtHpcCOjpkFVV8kv0zWSw4AJjviND10dfdzEBHFttkC0HuVf38DWz56Gh8XuN27h+BMnUJYlRqMhRiPJPs4y9Ps95HkBY2y7YrAHNBZeBXH064JDHb2BJwfjPfuM1ZQXV93jHy2FRLC7J55G9OengkX9vQ+XMAG4llenYK2v08mQF+x/WKZ3950baOI409bC2iv86fZRbs96ZJu/T5QZIGKEiIJVAu/Row4+omvoo4ugWIkAuU2L6JkulJzPrr8igJf6Wc7h/em/5fp2zrXukbqbFZPqEruGyziLc1Jzz+CZ7LNcgt43eMe9iVuPv8nVs299AXXTtPYR4l95wAMxFT+Myl1bCLUxpc/kWwwTYIGyM9hFZjPc2J2GO/Y6PlV/Dk3jGFNr0/EKc1vh9RK9I5hCSEBcxpXQKVU32AmEBXpBTRss4jJgCfQE46EqKzxx6/MBU4/e+GzA1Kg/Cpi6Tu/wujFjhPcey8dfC4qhB3Dm3sv7MJVlGZwzLWXMuZhrtLx0DidO/h28dzjdPA/A4x+v34CHxwlH+ytVHHA8YA8lby2MR5wYosgMAEiIrwoCAbb+1YUlA5tznSsS5h8ZXWoxyP18dGsEq0RDRRMxqgypkf4jo7LijSocBLZY/4rIoN/rxjmi5HpiiVR1I9nNDht/8i663S4e/Y3P4KM/+AFrLWNHS6i0THIK1w+joSgUW6NM7XVlK8lzEEF8ow2ausFwNJKwRLYOjv3KCxh8cxFVXaOUfJUgSMPDe8BLnSgSzUazZhUImuuTCITRt67Dew+b5Vi78zHgPbKfPYV6NJTe3RWGw7t4bLkDm1kUeYF+vwdrs7HaZ74FZiIw4QOAsQB88AHrmoTSD6JNPXl7JUS96Lmqdd158pkH0f6P5eBkvRoE4B8sAN6zVfAsfS7BFADvcc1fhvce2frLOHlEGm9J1OCJ6U14YVQ2y4RKaJ+1zMUZ+XgKn8Ei3uFvVFs23It9wbPQwdqM6Irs6uW2xIyp41ufg7qiMbuJJboCIC3ayteMmPJYwLlojTq2RN5v3hJMeQwGA3S7XWycfQfTHz4X9gHn1y5EF1N4ngMWlCjBVLTKUhyqhZVejBJ+AwALeAHXcJmV0HqEYfaXMFuEoeX2wVOTk/gfPn4cVVXjayf+Debd84GHjI+FMcVVLHTfSOdgEZd5/rQ6twgA74GvX/8l/PLZb4hS7Lhk0kdfRFlVWN/YYNdXZjG3/Txun/gBbDZeTzDSzvIxzvE5s3qe3YcAcp9LsmosAOsSa7/b6eLO7ZfgnMezJ0s45zBvpQ4ZpMvnfZZBj0MFina9S5YgMII0ZT/+5SNGUkTNMSZYUcI05XxE7SIQAKTboFoV+juHYGYedBjDpdu9z9rmmj6TSHUtaa/7LenvQVyupRqOQjYzQTKbvUf1reuwmeUGQiI444ZZtKbis1B4znEup8TO1jMFdUo33zEODIyvJ8+baxxKV6KsKoz++G1kGZfwn5mZwWAw4NwMLRIJVQ7kuuqaoIRxe3WdqUBrW3haQtx7j+qvV+CbBqbx6DiH7k8/jrWPr8E1DZ6YX8D6xkYYT6fbDbXEiIifOVl/Lrcim9HWsh9YiN94MPELCNJSMU3jJEOfBcrjHy2FKLOH6QgKGSB0I64Fp9GGifWWrPTKxhQg1ZzrI8xoz+LT0ZoEWJggvITiUj9boitSny+6shhTHkt0mQdzlM9dkDazzPsUU1EJWMw/QA5RDoWQQo8OMmIVyTMKpj70b+PMRxfwRPMinHNYnL0omGKnUJZZfDAtyXcyxmzjZQBckiYKg+DzaPGNMMcyAcEjdSCmFAV8aEh1uJgHXOPxvvs7oCZkVYafP5EhzzIUxTRuDj4QTHFeGonVEz0zwhNJCseKNc65bOp54WdcwDmAgP/q6Zvw/tPMX+FwrXgbH534IbxzOHH7RXw4/W0453C6+RSOLD4XMNXtdrHyyBuJtaj0A6wcfx3GS/IqeRgfO0d6D9y88Wn81EluM3IzM4Ihh2x7Gs3MOm4VuWDK4ESNB2LqUIGSsPEWY4sRJFHTjZYpJWFlvGieEKKBWpz2IIUjXDc13dORqBWUavUUkpcMESiLzaaiSEtvErvNBf802FzVYo6NcyGfgYiQS0VUeI9KQg0T5UgIav+zhDnT8bQUJTW/k7G26nw5rmFG0ZoLF/PJdeWFcxzi5xoWHJ2ig4l+H8PhEE3Tbvk7PjZKzKBwG9VkVDkIlaGjKxIEwBlYS7CGz7a9LppuB65uUBQF6j0uqNdIJyCX56HIplFfvWjXaiGRKgE+2W/xALyWZaGQDKfBCxxN6OCkXpt5CAUK0rkLR3zGuAJxU56MEUxFxs1rth9T97PKgns3dVMhGYZHoqhFSUdQl7BNTuQI0PTqIBEohjfwrdY+amHKo6wqsaIodEOER0ggbmMq/pvcKZkzHk70ZMS5C98hfh+YjiqlKR9RMyE8YVRwHDgHhMAuy4n+RIKpaBJSa3TpeCJ30HwqrxIvhHG3MWWckSrGFh5At9tFp9uFaxoUdYEaaGGqLEci0KWFiEkwJXNEiHtaXj7TNePIMa1VyPeG5bI3iikLPBBThwoUpeHW/kiijdPYYiPRescJAakWr9+Oad9EJrQsNSll6X3lTN3gVUGgY7EE7hWRXlP6uYz5opLrUqgV5CRJyCUCJc9zdDoc0tf7+XmUZYWtr38o8es++KHbPIKJRUffjv/m79WS8TJQ7wEPB99wgywymtiXcgiKRBqsLiFI4vkzQTB5ZJnF0V89h9X/7dtyg3iergO/iBEqKkDZFUytsasGZjRkWwWBjQQK79H8zY9QdLqwfYvh9iZGg11sbW1hb7CHR0+fhjUZ8iIPjZV0E985D2ujJqkWCPmovQc6IoJxrDEZ40CNhzdcUt24Bs7I+bB4mA7VBVKhwiTEb5bpCvSLDDlA0fWsny9sf5nLYgD76XoMUxq1ZYhQGNvSwsOGO1RYo0XHvNYA+QRTouj1ux2oFrngn8eiuL/m/fMBUybBVJ1gqshzFEWO6elJWGtRlhV2dgeYq5vEwj8IUzgEU8oSWAHS3urX5i7Ca1dYrVI8xnxiNOiY8im0Zjxh3j+P/+XqU5iZmcY/v/B9rK6tJpiKfC9iKs5prEBAAWsRU8BVuiwtN5IaZcbDeBtwyZUturDW4Fj2CK5Mvs6Y2tvDidsvYmb72Rambj72PRhjGFOwwZOkm/FEHGX3ldMals0WomIuO74LcpnUHWNMZTrJh2DqcAtFmaX+CZpkuyS7lxMCc42SKEjFMYUsMK6WFUIsyUmZBxR4Svg6lijV2+PVrPCokQQfZ9DwBM7SOKYoChmSR00etRPJTYS5uRlM/MJTAICtr3+I7X91OWrKCkIAlNxRmX4qxfX8ODUUvvD6bAQul0Dptb3qLWE+VZMcN1hS4drUNSYnJ9ApCuzs7GKwO4CHZNe3LCAGn/fx+uGaHq2y385xvoB3xM3IdD8rmqbBkoQx8OIuLcsavYlJFJ0uqqrCzto6hsMh6rrCY6fPYHtnG0XRQZHn6HQ6yIsiPnH0yYmgdCJ4+enZYCJ4b0DkAOnP4ZLeNw9b2DAAvHm0DyIoumyEAAAgAElEQVTgxbW9wDwVJwt4AQC7YPT1DdJaBzIvR7eVUOIRSJ4StxawZN6Flg5FoFeMMev2ngPAdZ10HyOlGJ533nPRvkEeHvP+eSm1YlAUHWjDixRTT+Ec+mcBlo81RrLnppjy3kvNLK6X5UGojqShuqnwiFaJHwODJ/09OCFPHm7ePRdaK+/HFAtDPRbxToIpD1c3+G8/s4Zebw83Wphi2kwHkBg7AIH3p+SaxhoO8fUe12ZfFUxxczRjfIIpXU4ew/VHXod1GQCP96sfoDcxIZgqsTH1Ph6/9QUOGNraxPbONiY3n0aR5/jRE9+XChcJpmTSjFGrJQ54/sxleAAr8DCrLyHPfMBUTnggpg7fQxl7F+yS+ytFOgvJG7//fJX8FEuMJ2wpXo9a71r38geMI94y0WsSN1PQzIw0mpK2wRwCzO118oykOxyklDZvyFdVJdI9vdZBA2jvJ6X31WdPrYH032AGt66cCB0VQEHLjAxXnxXQls0FiAx2d/e4IGEy1LCJSq0/rWshuR9/bHgPgxJrRMAaonyS30dr1IfkNGu5Cm2W56irGhMTk6hlXkejEaq6QjbKJRvfhDbRgQFoA7JElWQ57EOJH3it+eah7rKH6Uin9/6W/kGftTHVFggI2nQaAQmEotsJptrKWXovH7Aaz4jae5uZx33JiClKMOXCnh1jKhNMGcGUc47LtSdVDVL3UXhOHVtrGX3r735MxW/9/h/zd6RCVoNS2vdTCRwUO2NQFMyYB7t7qMXqkT4aMCbmAqVzCoyts0/ei/XjA6NmF/d+TLWv48HFbgOmen1MTk2hrmpUVdnC1M7ODhfklIrKAVNKKyStDLxPcgw9nKd9mNJE5cMw9YA9lDGivR8PTT8Mktm3CJfpNE6sMRTcWxAGpefo1YgOvJlcIWXS1KakZFPqIDeTQcxhMcZI6QIGRJGxadz5uXlsff0DbP3pB9z6EwpYtNwDbTPh/tOiLioCWuXV9/92v0kOr2BndeogeRU2HMFaU/er3PPk3h/xZqtaGcoEtLIzEZIumUlp6zDDscQ5UfqwPpQ+gdGSHvEa8RkolLcgAopOF1O/eAauabD9Z9ewtzfAsSdPoKoqjMoRiIacy5Jl6HS7yKVPuvb74BQM02JChhBi5GO/GV37h0ugAGyZMG85RCkiBG9nfeQSsg2udkvgMFXVehUvjfsMCIROdjniQTCllg7XBJOLAxgH9DgTpASLUJwiKhQq2Pm8FFPc8lcxlWfSIttcwePDpzAalRw9RjU0kdZ7SWYMFhtUGuybl3AoLkAHYGr8d/zDBX+Oo9MCpvii98cUv71u30VlN0AwGA5HY5jyCH1jwPOWGpDLdIV5j1xu8eglXhuvHpTIo7j0CbHyZOL+GJEKW35f1w0WpErw8rFLuHv6bbjGoa4qPHbrcxgOh6jktbogbz7yBjrdTnAxG0NYNFfCup85E5magShmiJiyoAdi6sEWSsCtD1ET45dr8ZnQr7otTlq5IDrshOhbseNtNe4+MiyIneRfRCAmMoaZDbtIsozzNkgkw+7eAJDNzd7X5jH85hLK0RAb/+8PJYEr3j9qQR77R5VoV2PEyL+NILz/csTfHyTJebOakk9THTF+5j3w8f/zA3S7XRz7tXPI/u37GAx2MdwbopRkyND/JNWEAAANUk3IuzjeNOw4bu553rxXDWeMQxKkn73Mn3MOe99cYp9+p8DULz6DvW8uoqkb0ai2UVUlHjvF7rAsy1m4dDroSK0xa9t7Yi2a8XFmWjkMD8kRRiPVYJNq8a2RBoEBYME/jxUgCPZ84zxugHB6djtomTeyP2ThIeG/iqmz4OuE1zwxACR/Ir0raXRXXP/gkpO9Bw/eT+C3LOCtYEotw4FgCtL7o7k3iWFVYX1yEzP3coAKHt8cYd49F6wArB8JocJxTNFCGpcv6d5eG1MHaL5h7wT7zxThoyHVADC/egGL0o9d2BTW1tfR7XYxNT2BZ7LPYHcwwHBvDx9UPxBM2VCo86wI8ZXNaZya2Qp8zK/OxCRJKF59fB4p7MhzS6Fw5NKxS+HhCaKMH+VQ8tPNcyGCr+h0sH72XZy++xLqFqYqPHrzs7h65DscHSYu5k6nEzb/U0ZOAJq578KuvdQS7A/C1AP2UMY0/7Gv1MQLWjvQMh2JwA2bTLwO+ajxBmGhb9FOglTf3sFDGDMNxm3d9BRKrKKMH1kTABvvYW2GLCNkNsNwVHL73KT0BVMCYf9keEBdMDrgQP+RSExLC0nmat+6JAJC92Ak1yBWLYZoR+7AxdU5ZBdSjeEf/h2KosCRX+Yufltff58jVBy3HyUTtf820aAdD5AQM5GOoQ1lBXgqpFR9iGVRlFnx8+z9xSIDyBI63Q56v7CApm6w+2dXMRItq3ENnpxfwHA4bIFBBaPSmo7d+2QmHzoD5QDp0foqpbnkK3EH8dQzpmzCbFWoUoIp/tMWGIE7AphPNtP1hGuSs7CweiE5la3feX9uH6bIsDuLEDFVe4+V7H2uX5X1sDWxHSpBRy9F+/AAaHYzPE949MQNlkYomTHwxD2V9ufz/nks0RV5FskJcb5FwwCH7o5jamH1AhbnLoZpe2Twq7Aji2w3w4e9P4KdsEAfmBkdEUxxXhgZg2Z9CkSEcu4inHue12ZtBmkfBj+ONyIsHn21RbILqxeg+SLKc5VLLpsrEXPyqXY1vSZ1wObvvYyVR1/n/kd1hanhFKqqQl3XOPHxF3HjkTfw9NZPIc9z/OjxN6VdhQmKuWLZe8DKTBy2LflAl9c4Pd73u2RSgn4jr7U6cbqZE5YtuYZP//r25wTV8O8zFtXc1YJQKgi+QiMSP5qqTeNQaVQVccZ9TPw54NpjY6TWJwdZJYnm0Xry9sOFbntQmdS2CtK9lU+qcGtGc11XADyKpg6upCbPuRNjEtVGCSBj/FgEf+oWC09C7T2N9nPGJ43Mvv0AYTNWzjaWW75a6+B6PQ5plL0VrUeke1nOOeTSIVMbNrUl9EMnSQ48DnJ5BZ6dWIQsOKOipsEsGMNU+wUfgZYOkK8HY8oHRpvuTaQuLq37phFkITJSMOWMh4YGayHY9pg+GSFH5SplZindHPyjNqa8/jpMRAiFV875CY6maYJbqbRlaAAWMcWh61qiJlocOqjYIkBWch+z40rFca4jtkIBF8FU5Lfpg0dPCMB0YwVTnHFvyMLaEapqP6b2hkMJ5bbS8pzCHly4/gPm6PCwYX1ItImN/yWVGawxGIRqwIakZEa62Yb7EDzPsxBsUq03vbt85OKJXNaZsO/8wPDlhTGQnvCaVQrReh08GVR1iU6nQJ5n6HYKWGPRUBMIQiXT/siGJMordXWpZZFoVQcxaH2vbqyWWJJnVMskYc1xPZIM3XbMF6JWJ5rN9vYOdv7wB7DGoNvrYeaXnoUxFqO/XMb29g5qTdBq+a4ROm2GZZO1bs1FOkT9bXBFyCkHNIoKz09JImXjsffni3xra9Hr9zHzy5+SqgU1Nv/NFRw/cQqjcoTdwW6oDNDtdkNZCtWmPgnx/zgOj8js9qkp8oWHaIeCKWXMiqmTDcEc2Wxx1AWci65eHy/pkDKi/Zg6458L5y+ZK+Gra7MXw9AiphRU4Na5B2Bq0byLvXKESeqLJVnAGoNGLVof6dl5KXB4gB6QBgP4CAj9KMGUPlHExrx/HiEhF8A8nmetnS5D91DRwgyx+y8Zx/zc82gRtQduZH8QFCe3zYEu1lh0e130et2wib29vRPOm189L1nx8Wjtxe7DFLUwtXz0UhTqiaAg44MinHAFUf58qBrRzGzgVPUsCMCSvYJev4devwfvHLaOfIiJ3QnYLYtROcLs4nO4+egbyPMcz+1+BR93cjwhLvAV8mgOaLA4fhxuoaiGjTHtUk1vw5qSNZbdWyJ6vWsOvSyNM0Alj8B8UymO8CK6yrDvCvvuoQyMZKOQ+L7OOXgi1M5hMBiCDHGJlaqWsvsEELVL0QNB2wCpFh/HJXERrdkeF0DB1+u16mmc4/b8UnitG92R1MaumbxohSnDSwc+GYeAsnEO5dYWdn7/+1zRtChw/NdegAPQNDW2/vR9NLKBzpVoTRSIKdAJkkQYJpt/A+aJukGq9/eu4f4QqlUna0lg140nDy81xsJ4G4fBN5fDRE/OzKD+iWmgbrDzJ1dw7IknuYjezi6ICJ1uF4XutUju0Sc06P7/O7yX6LioHoT1ZtIDCaa4hiRrrCem1nFjg2tJLR29BENgF1S8MMZsDLmdRwKgcFCCqeDlaSlo+49xTJGAUwVJ2ZQY7A1BZA7AFEK74ygrfGjlvYAYWgtKwv9bmBqzcnykMYIKIRZcWrZ+wZ8LtLaAF2CIsIQrqVqsTzc2b1JrK5lP30QMK4NnTFV45PpnkGUZbj3+JmampzBbzaBpGgyHQyw57WPEUVR6jZONuid5Da6TCxUKAGDx6EXmLQFTWoOMw/cpdS/r8orSZsnAE7Bi3sMZ9xw0ivX03ZdCiaXFo5cwOTONzSnG/WAwgB1ye/HVe6soiLAmmHqs04X7BJh6QOmV9k/bRRYpEH/QtsaY6IFRWi1F2kcZBYTwNV6+9m/HDQQ66Lsg7yj5XLRfUQccgFFVoarqKByEONSE52EEvSyMNbUV2mNLP1E2Me7+EWsEce4w9lqBn8puFbQti2dsANE8juPx4267sfE2DdcAW/+TKxz1YS3mfuUc9v78Guq6iWUlwjP5FsHCJsIqGeOY3oE0fMHDh7LswYvICx9mKPiwiTPd+TFkbjxQfmsF3jsURQeDz/bQ1Dl2v/Eh5h5/Qva+HKqykt7lnJj1MB1EhO8f6wMAHl3Mw8b6OKZCfE9i3dezr4XX6aY9CKGu1oLscwQ6FUwdGEjiAazP8OvZzX0KFPT3iDiBWAeKKQLhKr2DUVkypuQn8+559FwXW/QRnqIXMMQI7/u3sDj7KhbWdX8mYirs5fix+6dzN6ZSKcYW/DksmytBgICAs/QClI7Cc7Uwlbz3Hmfc8xKkwMc1XAY8Qr915z0Wj7za/qH8nV97GZ4YU0989AX0ul1UObuU3vtcgaY2AVPHrmlIN+GG9TjlBE9EOAMDmKjALaxdCN64a7OvxnU4AFML669EhTsZW5o3suBfgTONfM5CnFZnAHFJvj/175HlGZq6wZt3DB7PgFOCqcW6xLzNH4ipBwgURGsECH0RAuErm0xXucWE0FKMUhYdSGMsQev/Y+/Ngy07zvuw39fnnLu8fRkMQADzZgNIYkCQBAiAGFAiIVGWK7FcAiVTmy1bqZSTlF1ORIti/kiqUhW7KouWSpyKU3EqlYpKkUWasERtLEqOBMgiABIAiR0gQMwKgODMvHn7u/eepb/88fXX3ee+NzOo6JYxcvUPhXl3Ofec7q/7W/vrr2OvRNNkYwLF18XvvCuItgchlTQbEYcEWBBGZek255F/BjP7XfWEaJe6n+D7PDyaoH6K+UlKY2MbrgrxT7X43eCHo2L23phVycYL4RFlOfwknkTx/VUh6DpSVVWwTYNev4der49etweampIU3tFINp253ylNfZsRl6SHeGgKX3WDInrIdY1XdOw92hitEJsxMK6+GDPCmRMA8qLA9PQMAEaxuIh+v4+yLFHVFYbDIYqig6Io/MbV6wUSqXRriyY+D56cFx2Up4da4P4mew21YOy0MwFb10Q85dVULFj39YAjI0j5wvGUMndDcnyvtdaH6FTpmBZPidHhD9FjRA9vK7CIW6I5FMmN4OIHK93dLn4f8xTrY6I00OCRWNjxychqNAUlKo9WA0/HUUL9bIWnmqZBryc71nvdHrjDqKoSo9EomI0ESRt2NPSL3+45kgMUeEpPVg38F/hcjgJmtGPPATFPtc4ionYF4TwvUBR9AMDCwgL6zQhmWKGqK+yUA+xm3Wvy1DXThuMjgP2GKZXWWjs/doMozlXeZ+LvMTyCK+eJBAJRe91iXM6GewXhKsQOhwIxM5q6wqgqpSYNEba3d3zNGz3jWqIQwjhFnqPMMgBle8LvaTMQL1ldyRnz+zP2uyC03N3VwtorXO4mtSr5uGX7em+6YVTPkBlrJzO574DRqERZltj5zScxNzeHXreLuZ/4MC58+TnUTQ1mhE1OYaRaBofmgMi9OcyVyPqWzVZSxZRd2uy4V6NCivwz4Z9rifw6ApPFzle/iyzL0O310fnU+7DzlZexu72NuYM3ShXm4QC7uztXpPt7gZin8jxYe5odqVk6Le/ee+6CfO0BYGHTv2eG3+UdRihSHBqsj3jK331xHfrDIy7cwsw+ffc4PgRDBuey76BjpPJs7XjKOJ7a2N2AMZmrBh54StdL8ryAySocK09oAwFA9lFoZpeDlPEPBlcbIZ02vuCcedWti8ArU/XYDuODeIO1wjLhKN8plYIRkZSAU+Zl91J+f2T1gZZSJwDH1z/h70MxD0pxbDmszD2/LEtUZYlb/yLD+sIsut0+1t7fQ1HsoK5rMACTGWRN5I25iE3IYMtwelnWsnLOI55CxFNydsrp5ceDJ3L5E8GQj5jrjKseoAba2bMfwqFD3/AVvM+d/jA+NTONLMvwVq/CrUs3YmdnB7s7O+hubYJLe02euvoBW62zLEKBNI27Kh1UrLbku37my4kEmsW2kN57f9Xh3rOr5+Zvz63JoCEjrXaa5bmznKWSKZnMu+QaxlFLiwwhyyVTaPMPX8OoLP1GxlYraPx9fCQtXfXa0G9A02qjb+R/grc2yN1/PNU2pl/4beT1qPFmwqZEwNWf0+uM0FvbHVeNZsvY2d5BVVaof/9V3PKzH8P6772CwXCIwWAYPA5VEm4A1Fv14zLWPV9a3rodty51my2jdscMqBAdD3WoUgJkAVQPBiMKhwGNyhL1H76G6f/gdkwxY/MPXsXujvSjrCpcT4iLMx6a30AjR6PgsXM5iAweOlTv+c0pvIyjuMMpDcI5Bt5A2NzI44xHhNOQEhpyTRiQ0/QyjvKd8pvLLtxFABbXW7Q/evkkJAQsPHV78YPghXXoIUwxTx3mE8gog7EGdHkBZnkTueOpm4e3Y73cQO3Pjh8zpDXkBkjJ9cgjibFf+Fzbe9SekIQChL0kWrn7FF6KeMqtdUY8dUrDXEyhwnLEU2055Yw6BL7393XRDJ3D6mUQM3Z2dlBVFWZf7mH2hgPY3dnFcDTEDTsjObaaVKaG+4GAM0tPuDpcfAWe4oinXH0+PRuIRe6eXnoieKPaF6dtjhx5EXDn1jMzTJbDGKn7df8Nx/H93fMoig7m5gt0el3Mbg3kiIqr8NS104a95aRWTmiW/rsnps+tH7s/1Poy/CTOlgrX7XePeFUgrLLIoBvSQnhCWEmblUkspVMaVFUlVq+zto0xsrmn00GeZxgMR+5grqb1pHGhPt5WfR/arB5Wm9lja1sZRxScdSesBasuplAsVFvEo9YfPzzj6aTxdzHGlbNlSSEEgCwz6E9NYdTvS0rh7kCUURQC1XBaeGbULg7Wcbzu43e9A2DD4FqzvCJLbUx4CDWixWMGDIyUBHel7OWkO8CYDL1+H0SEsqyQj0a47qA8pYkfFD6LrwnTicf4gb0XAER0i34cncKBwHvwY6LfxrZJTH9vCZs2T9mYp2pZeDeZq/TsUoWVp7I8x3A4EkNOq034PtAeJbHf+1aUIhLWvqd+frG/Ro1GYuy9Hu37RTdqk9DNtVgMhxIt4RLtTYzgcQDMFlUlo5EZg6mpKdcmC94e+lCif170Xrw9Dg/jMGZxv3QpAhCe0uOC2beF0KokHW4XQuJOHmokQk7RtKBcNkD3en1MW4Oy07kqT109bdgyGI0nXJYZVy4Azj1T4d8maXxEaIsacCe0R4K3PTnCdeM2fKxYwoKum/gMt/7hcuKbGsPRCMyMLM8xGspeBh+/bxo55jTPcONP3oUsy9FYiwtffh4j9zsa69tVIlbjVENQQC6EgTbzs9vgpdVAaSwPPkwaN9ZuvvlJA2WGdkw1ptt4PFw/j/cD6BqIJ60z3mSDWo1OUWD+b34Q+fYONn/rKX90qb+nTsbwQSteDXYH+EC93cxlX4X2EJEcttSEjW8xo8bXkWcqgDKCsXIvZkLTNNj+6usgInQ6HRz4yQ+jaRps/eFr+4zRewe2jI9d3obqXxEahIdW1DMJ8y6S9XgDLwbBsyDXheAj4xTCorxY6fJd7MkAAJP1axgaXxUbR44qaMXbKRSabJoao9EI1vHUK+W3ZNzQ4DZ7l1xvCMUN21iYX0KW5bDWYm19w/OUn79LGwAItLGAU0thZ/zV84ciGqpCZO3ji0AT85TFuHJSff0GXgoCG+7UQ0+toOSMq/Ydy7FWeReV04Rw0F4s6JXEAG61hAwWF3LhKdvvwzYWb2VbOOrK08c0b0k/z1O63yuUUNL1qvGMxnd6uecpyaKTXh7jO1vPOu0SGdT4M5kR44CBt7tfBm3fhxtHFQjCU535eVgru++vhKsqlCw+iQ20Z8C95RkJXz/Y/p99MKZ1/evwy9ZP99zLPVMWMmU9pCgKwMoxmjuDHVCew1rGzmAbVVnKBiRXJG1mZhpTU1NYfvgERmWJC4+8gNFIDtPyi486xfwAt8N8+n4/1QcEq7yta0O/JdOqdguZWVQLy8l4lqQB2XRIksIbudjxIuQeTwXiramFQjqODElZRUgp1jGWr9wBXEywtkFdV8iMQV7IJifbWM+IjGidBGKxxtlK3hKKlAwBntkttJS2kpOgu4it42Sf1aMKyGWDhYrS+oWByVQwMkajEtVXXkGWZ+j1eriekBmDbx+YBgBsP1vhhw5LOObRc8KKD63UzpAgnKKXfCgSGPMogKAY3LcAcIpejHiK3GcvIQa7o2zbn837cZPzx4WneGEdVdNgZ7CD0/warGWU2yWqqvSb+grKMTMzjempKczMTGNUVtjY2MQw4ilA+nOM3a5xEE4tPbGvF3IljHsjLbj7iGGi1bDNnt8GngIoy9RXwvHLDwJEsmZB8j6etyB4YQu4MkOMNk85hWLcGS9g+LRoZsKXV2fxHx3aBRjYHTBW3jHBwHPCxUKPFQcOX7wfp5efbPGQKr045KZGqYVLXJl/HLR6P7xEYAvLhO/SiyAARy8/KH/pZDjKmBnHVmqc4icBMI6tfkL6ZOQ7TXjJswzd7pV56poHbAWChoG2DH9eiWWWMzyiU8Dkcu0Mt4SeNl49i4hMwB5LPWpF7O2QZlbIIVl5LgvClW1QVhUsE6iRyVVXNYwxyDPZMbr08B2YnpqCMRk2/+A17AwGKF2Gyh4DyTfAifI9ysF5IQjWfnDcNPuJ40vh93W4jwKd9iN9mDziNenn7MI7xsdb4UNl7ZsFPR3WN8jtnPG5Q86VJyK/CVEWX8WiKvIceZ6jrqux8GSwpK1jHFXy2sbWuOtn0ZkQcTt1kRFxdwjBJY2Kfgpt2HtrZH1+lM9kaRqLkq+vNZSYp4gIj54LB1fFKeyNld3mYb7sZ78H48tinKfIl2uJ58N+Nh4RgZY2QOuLcuiZ4xcQUDueaiAGhVRfqHEcH0JhCnTyAjMzU5iamkZmDIbDEjuDXVSOp2L9cMzVAJOHanv3tzrbPNWOGKj32+apQN2W7NlLem/0MDOOr32iRZnja58QxXLgCRxdfcBT/dTS47KupI92iiXobrdZ0inLc0YUxaGG8M82F+WI7oKx+Dph4/1Akef4XjdDXVU45ObuecNYcXc9tfi4GHBWeSr0Pyaq0Mm2vSMhr49eHLn0QNROJ7OIQh8uz0vTlzac5+yMQlcyixiwVsJvjbXgv9QairQidEBjcwRfbqGpxZpu/2Ts/Zi3KCziRYMTb3tZhqJXSiAiKdORucySLMtQNzXqpkZV12AQbNOIB8AW3aKDTqeDbreLmekZdLodVFWFnd0BdgcD7y62raU2I0q7w3dqqfj+jLm6IWvNXaGfOwFBzl8OZFJhG0ROK/0x3NgT1EecaO9itu8Bw59sqL81LgUxxHrJewQUpU3XdeVi4RnyIkdVlZGSoFaf1OoDMazRjkchMHetLs7H1rZme6nwi2P53vho0b3VQ3ml9eLcSY66U7is9i5yXxdo8ZR+Bl+mpG4salO3rvchL/mVVyIybdpqRw+a2qeObnTLyIt0mY+6+z3PMtR17fmKQcLrzqvOiwKdokAv4qmyqiUraDBo3fvqPBW9jga3zVPcutavQ0ZVpcO0bK/NtHkKYzw1BvWMqTU8+7RU3ocITVtJxUrNjG1FqOsKRMJTnOeoytKPP0ePYqs8Jbxh43qInk/kvSzO63nvKlO99RgMWwRZGxu5gbDwhGpHX+SsFj1+u6yvzFPvSqHoQFsOGng0qlDWlduwY1vZKyD4Kpl6YIxRhgB8FU3vkQFh5+7YhAidFCtFF46KIkeRyYl2dVNjWI5grVQ+HQ0GaGwDAqHb6WB+fg5LD59Ar9tFWdV4+4vPSoG0pmmlt3qCkzIqecu+iXPC3Qz13uqYMg1urPwT1i0klKQWtMhRihY9a79grU2KFxvBLnMEY9aa1WucmuYweciQO8860NB/R06Ru9TiOPbMljEcluj1+iDIeGmIyt9DvSa4NRLnUoRNlRwxi7Q/nvzjClj3mXjl6TJZ4CxUreSqrksocxMpT0e4zOTQbLDrDfdeHgAA/oyzaH5LEsGLw2dQN/vwFIJHemztQRARqqVN/5013DLqYkNW73Hs8oMyHn6KuX0iZPDm9iKKXo5jywMQAVVT4+XhMzjSnIDJMtSDIW6xt4EIyDoZFuZm5RC3bg9VVePS6pp4Mo6n9LmuBcGbBOGUy0CLs2x03JRX4nAVgHZoyL8Pr42RDX/H7AnPUwygvjSNsy57TI00TRvG2gLYhA27AHw1jCMXH3A6S+bekdWTnqiGCG8sPo6jqye99+RDUE7Ya4bs6cWv48cPihdqiDAaPoyPXqrAlvFNMrilZpwxLjmjAV5felzGasAJt+4AACAASURBVPVk8L5BfmOjjG2kYLltpDEzjq4+6CeBvfhAOO0UUopHr31sexdmy+DIka97WqqyPkcSsTnUuKQnQwCuzVPXOAK4XX2zaawvOqgF0AC/5cF3COwW3wkg27Yy481PosmNswiML1NunKDzP3JCTDpm0CkKn7/fOJe80+miquQUuLqu0enk6PV6mJmZwdzfeD8Gf3wGO3WN4XDk224Cx/nJFC9mqxCOw3g6eWIlERdz09vJ4rsIea8MIjrES89a+E3OrA/0D2clqLcTLFJDoTwMZVE812UEqYERW0o6RoZcLLgVV0IIW1oLiwY7X30dM5/9KAiSwOBnsP5UC9aJn+y8Aj9kCJ4ZIZYeWpNNlUqIypDfG6OdMDbcSxQRuZLv3jyJCKZzJchpMyaY3msw2+ChQkJK/phcF1ICNNU7stIR1kyq2q2zjEr3ucWuEa/gGN/pjTgiQmdzGW+97xlv0FGLHG5uZAadIkdeZDi/OYemaTAqS3SWOzhbvYpRWeJgc0Q8kl4XszMz6HZ7sNZia3u7xVOxZwLAp6UDYfrogVJ6nf4f89Qxe0LWfqLrdG7K9WYfnpId98dwwq8H5lkmYSwGeGkD2dqipD8H9zo4/eT2OUVGno5BbNT7KIm/huGP1XY8VS8+4TjL+HYfXj2JNz+xi925BcdTJc77o6oDPwDwyQpHLj0AZkRnGXlytqCGV3wPpYpxRv45Y7HC5CsRY/vDwutNAyZCsX4SjavGoDhn5F4rHJjqajx1zTPl/UIY2GdKaefGHUcd2MZpOUK8rhKEou5nESEqi7sZcVQfSO9HrRsTQmobIPWn6sa6Q2lE2TEzer0upqb66HW76HW7spBYVSjLCmVVt91Z5kgI7l3w289dZ5fi68M58HOiRQ8ELzVyR/f40pGbbLyp3W4FtV779jvmdA6w+4za/XPWIRBbjPC/2Ovaw49Z44SdcVkisfcgobR2/3SDFBktIa8XxHNkfBE2dDHs8HbEDbqu1WrRYxxOstSxcUopEl/7UPu9hQgH4Y+6EeVgG9mnY/UCb5lEPGUDT/lTDn2WlkXtsjErrpFx2D+y54waojGaaMmhvTxlI8Ox1+1ieqrvz9Cw1nqe8mWMfB/bPDU+n/cLz6qcIYr5bB/JSdTmKffZ+Eh7Y9dZF/6ExnCjlgcXqBHfSbXI2CwiDb/DX93iwb298/2LeSrut++Pp5WYHUH+wv8d1yzjPBVaJTPGWWiiCPU4jKjfwlNi2OoG7yCv3N046t9VmOqaacNKgKppULvYGUHCD7GgaLnbVZS2R4HkRAQ27BhIWJ8BZFmGTi6VST2BIyLAdU5LHBDgalGVaGwDkMFodyhWCQFLS4s48JkPwRiD4ajEhS8/LxV1nXXD3mrWQbKt96FfoU/e69DB9/eIY/6+qdAAkEwE6zN1KHOWle8bhfdjgsRb79oAhI1xPhNKFZBTVkSSgbIvM8Jlf4UYGPZnBfEoxHhgwDDyH16B/eJFScjQjZPsslki+mn/W/HwiG4Yo26clMDRonu8FiDeKfl2KwP4daiov0QI1c6vQzCzn4sfv9HxFDMeO1fIwWFeMcJbliKTo2KlixsgZpzfmAcRcOvCJmBlFE7hJdxGHxaeKgp0bxq0F8OdJUQEnCOLw8jcyanAzTNrGDmeOlcuoHz7btwytw5DwGZ2CEdvkLk1GpV4bvtx1w/GuXMf8f17aKVxY9HmqTiGfxQScjodneehbdQNig1bnDl3l1eAh1ae8/aFF6465oY82Y7jRDBkweDF9T2/w9p8a+6Nr38gur9GF3ylCBL6x4U5yRhvTDFLe/Q0xVNLj+Po6oOw1uJ/WpvFP7m0g6WmC2MMnmfGLbWGkcUbOGLvAEA+M09CXa5/LZ6K37uqxkJgnDnwJI5dPunaEtZm64UnccoZbkSEI0dfdJUnhDa2seDGqrCTjceLT4IIeIOBo2sPXpOtrqpQdsuRnA3tMqBioaPZOG5EQml5wCmbCCYSsE7TEcmxlHOzM8hdbFw3FAofUYilgaJ6/gTLFtu7OyCTw4IwHAxQ5DkOfOYuzM7MIC8KrP7uyxgOh3JcZ2hoNBBBeLc8KJ+ttB9FYqUTGwpBREY2NkTZuAmbGa+UWvFhrzy1bSGMRGFhCbHyYv9xtC7lBKtfBPWWTpgg4ToCIQcRxjZxIjgHbiFR6k0ZdDqdiE7kz1GRBVyJrXq3e8zlVpops2kHfOgjujxeaBayBOuraRpvpXmlBkaoeRQW7a+kUN9r7I5GGAyHsMx47FyGT67AGwuWGYetCKrT9FKbp6I5c359Tsbf8RWYcYRPOJ7KMD8zgywzbv9Io9MLcFEAQJ55hAoULiX8lrn1Fk9t9v4M/eoHsIVbMTszg4WiwPbge56nKgrzZmXlOU9va++Mppv7zM/pNi38Tn8E6/uI3/3P+NShOjI49FqZR1oMkohwxN4RhUDhBT85odVoOrwqDdK1XtozX/3nAGh5E2Z9MYTbnNayl2fhF7cBf29jcpxefgIrF+/3U1rXM4iAf7y8BSDzPFUUmZdHROTCnrIGdZu5C/bynCiUVhtjw00jAkbjDSAGjlx6oFWXTH971KVCn1r6uh+fmKeUvgD8OqgoesL5jblIplwZV1UomtMdiAZET8Z4hCKYAa1+t90lAHkmZyl0Ox1nlUXaSkwHaCBcd+rqfonGStXOLC9Q1UKMbreL5c/ciU7RgWXg0u+8hMFg4Mpnt9eB9pCE22+0CJu+j+WS9t8LOv95JMTjie08hjgBQa/3z9bJ6+5Jur4Bb0xGd4vusedFoHN8bK8PpyGy0Hzf9ipIcpJKzgXX9RjCv916P26lJ9HUjY/z+7x3a8diyKFhYjzIezUUrFPo8XSKLTD217a/V0tUpkiw5oNHG3oxptmvGzS2kZCwG9zH3P4TNbbiY2i1C/maxNEPKcMtbrgfOCONZK0gyzN0C+Epcsr1/IbsOSEiNMuy7+P4+g8gc/u3AOHz5sIUsoVSTlZkxh2duzF7hFAUFbJM9pS8NHgGVVWjYUl4USs93ueiFXu1qcf4xNhmPdYcEs/qsUfmh8sJ+0fPSvryQyu6054AYjliV9mIok2T3D7NURKCTJgHixuyQOWeRVHpl5jonkUdrdky4Pbv+Pmmc9RHLeQ4Xl7cCHxweV5aTQa0vAm2c56nji7v4rHhE1i5cD+stbgVQHNxBpYI9sAWqGkkOSAyTKHkajGHJqgwmICnj5dK6hY/EhHuO9VxSs4pZtTeqCNjUKy7em7u5lXzIde3c0Dr3Kb9cVWFYqPMpnYHIiJ63t3fGWqHOkQzy56QAt1OITsiWLfa7bViDIXMLmsb1I38nxeF2/Fu0O/3MDU1JWslZeUPjRJhGlUORrsP7bfBgm8Zt6oMmYGx4mxeYztByZGgkHvJlz6l1ZPSeRsUYrwA/NqDPjiS/Yhu6D7YuzDGYFfCJX6YejRR8oDe0jGGWn2q4LTEStNIuRrN3tLECK0fJLdgNNzAZME/01Ad++dp3Fa5lF011b3WVkw3nXMEybzTsIMMBbklhDiurBZj+Pc60ydhkToWXEH7w1MsMjbGwdH1DPi9WEUuPBVi6hFPeTcl4ikysCwliuqmkaSQFk9No7EWI8dTQzuKeErvHzfIvR17rUkbrc8dr6i3ThQZZlB7MhhefuY7XhvnKZ0sep+Yp3wz40Z4pRbN2bHFZqGeGF5tOebaqAeieQsQsq5F7ion0L1CJT17yfEUaXKBqy7ivG5mBjcNMhvHboM88IfhqafFIWFDvTLt0zhPxcYu4BJ0Ilmk63i+Sy7rTT7bfz7GuEa1YeMFsV+AdQzg7MDwubYgEjTMehayTOqMCP1OF91eF5pRIQv80STiQCgNcYFDORAmAmU5RqNSUoF7PXS7PVx85HnsDgYYDkufKUT7DD4QJpb2wfeJGTBCRGvdjlfSQQprOyHZwP1PFFUVCBPPT3z/TDdFo0HWs6LjdF1vuu0J26gl1GZA78brc4mirC5l3iDc94DDs9TDsZaxvbODc/+PnOD2oewZ7HYkzt/28lR5tse9ccUA4ycSXNqwU3yqE7WgnSoM6ZsKw2iTJNCilUiaqO37hC+I9ire9xKhTS0Lw+tDrRp8Ci8jX/s4AAqnW7s5al1KPBEhM8JTPcdTejxyLDnZlUkv1k/KIVAZOcatcVPlytCbHK+Wz+LD0/eh1+uj0+1iY2NDeGpUSojJ/RfrPyLgKN/pM7ICT0U8A3hZ0EopZrfXwjFhzFNwPPVDh5vWvNUwrOuZV3BhggQeUp6w+n5tAbywHsi+Nj82l90NwGgaDsdi67gd2JLnrs2HKEBstLp7nlp+AprNhyUEw6ABsl2Dbt11x1fnKDo5Lkx3xoxAAoY3oLnpycibYDR1A9ueNgAYTx0f4Z7Xcv+cpfWnw5otEVYXPgYiYHn9aZxadmfGMEv6dvRcs3YSK/MbYMjhYvmapEWzZYzmvg67ehIaWr8S3lXasIa7mkbDX5Fm12td40HO/XJCrshFSMhxrTm63cJPJGa3khjFNP0xwsbAgNBoBoyzIGqXHNDv9TD6k7OojMEGS0XPxu3Yl7TYEEX06yLRwLczK0IfNLt1byZEexjlOzhKOMvPaJZWOLuDlYmYPQPGC/tyj6hSqPIGa1p25hVEq1SJu54Ie8pMUHRREPS6x4NcqQi4Nod1JaUZOe+ArZWzHbiHW37mYziYF37DoF+wRBB0XuEBftxUmUnb3f4TJ/R0/UbHgNli+4++K2PvylcE5R8OdNIxbWzTWgyVPo4rzOtrH4qedKlGU+POVT0+P43Tm7v+NMF8/QF1GB1PMc6TBQxwOJc1q3e2F+UIZO7gUHdLhDZbnHZ1vYgIBR70PHUYGTI2OMMSyrZgnMuMbGCsarw//wiYgdFohJdG38LO7o6EvQ3jNtzViljIyadiQZ/CSzhq7/TC+XQUtou97GvxVHBMgzcrZ+KgxVOqmPy63Pp8EIyLG8DaPMyyCH+7OucbYdmCV2dD9puJU5XVsZBrM2NAG0vKzvCmEbkTWk2o+OujAq4JRy5+3PWdowochNPLT+DIxY/jzZxg+j0sHjiA5V4Bvtn6Y7APL2x5up1dfxij9wUqfW9ltC9PLWIK2zdksI3F9w4PsbQ2jWztAQBANf84Ngspfb/zvgexdPlpFJyDGfggPgrL7Ev9H7lhF9ZmsJaRwbh08hfBDHQ2HoyM4/+f+1ACHUUQxd55RPuW0eu8IzBETGYmg8kkxptnObwlArh9GiGNLbi26sZZn2VmGejkBfLI1dMUZnX3RAnphDXuM7lj01i/ANXqHoeJIk1gr0DjiEPsju+nVFrfUKQ4OCKIWtqekM56VMXklU1s2Ym1znqq4Vh5Gn1qvEjYGpBxL4fD4qK8HR9VtL63tbjog+EI1BfFqOOB6HnMofSKfkxRXSIAsOxOkDFSktsYLUgYEhaqrljaxq0DxET163l6PxuUmyoU+NfBgrz+wM4r3Pcbh+DXCU+5ZASQL49SFIUrkUJRf+Gt8+A9un+cEGq8QnE8lbtEDar8HrMaktGphl0GA+NKKdI4T6E9Z1iFgPYp8qDfDU8FpYLWZypcvAHjeagllaKfUKDNmPcaL87vl8AR5BK7KExoj++r+1/Lv/vfcsyLSorQhqaRU0WHwyFM33jFwwzUVSgS2lR14Cn/6f48lecF2FgUBaMoChTdLghA1uuhG/FUt9tFl3v+no216FEfRMA0T3slNY1pZNU0pmdqeV9PYapBMPKugGsesOWFHrNfNBunv7q4Omk8cZ2nMdXvy30sO6s0FCT0J5G536naivPgrZWFvKk8Q5F30f3Ro9jZ3fW7ZTt5hoLVkpX7aBkJPR9l/SuvYDAYtCxp+FcuBKMB7VZYRftIsd4J93DhvFBOJFIQUCtFZ55o97BIHywz9TS4Gc/okL4TWxcuiBRApAT9ILPKFKVjuySDKvQQOgqrKuQ8u7jXzBaj0Qhv/cunsfBjH8To35z1fbXqsRpC59OH/VpX+W/OSkltY9D90SOo/9/z0HWa7o8cRp4XvtQHW3bH9TrP9G/eIUrUyGFn5CpIk5FjVAEJneZFDp95o/1m9swZLLnrS6GIFezGTHmKgFObuy0RGngKEU/JHD2bvYqpfh+39XvBOGMTFIpbbCeIhXp085MS+oEIixqy8G4ZmMoyFL2ujD128Z36WQASbu50Os4AAXKbS7mjLHMJADl2dnaFpyy7s+Cl7cwSugtKzRl8YzylRpTCjyWph+wldkvJqOevi+SWLcyBLfckApY2gLVFH54JRlpbiTXWiicyrlRIZRG3po9eZ1l327Ln4BC2p4ij2Fc/BuRAsdPLT+DIpQeAQYnsnVX0+rs4tfSErx7R+/4H5T6G8PLiNu5fH4GMbOJ+fWMaxhgsb3wL60v3QwtSEoB3VuQ3GXL8wekfw739C8iMwdzNn0R/NPI8NbzlU3j6+QpEBoePvoCmbjCDGRgiLOaLnqc26ACy7AAOzBVgZiwv2VZ04kp4FwplTCOpRnEzwTK7zNSwI9MQkLmCgt1Op1322d+EXLhFhZssLGbGuLMW5HCkotsFZRkqd8jTyBiUW9uomxqNlQG1TR65YzJxq9qVQwehrkuUZeUF0ngfg0ANyq11TUtZBmaHe6+CoRXyiyaVbUIlXkST1y9WEov9ZyT84C0qKzap6JFoNKJwU/AK2L8OFpmNXPrgdWkcu+0Nafn/KNQE+EVNay0uPPLc3n7os77yorecmbVMCIAvPyujrTR8RLJmyIVhtF+6JhBbk3LyZtiY5zeDAV65ajiQyETzFej8yGGv2K8nEAHfWuoJdc9VfgJ+6lCNPz+fI1/7OCwDN8+v4c2NeU+jw0w4d/ApZEWObtEFM/uTCEMXySd2KL1v3/hBOR8j4qlbO12QMRiMhpjuXvBlSr5TPe8OoyIcru+IUrMBSwzreOo0vQweAYNqgFEzarkdR/kEHj2b4ZxrjzEGn1qp946CkxdtR9dbSEDEU0QEXp0Ngsxa1MwgCtfw6lzLSHNHfoo8aQJPWSvG5h4FpTyl9yByIeW2lxPWqOBlAuDmoxoK4zx1YMt7KSDg9IEnvSdZVzVok/yzX+SnPU8VO4znTebkJKEYyHUbALD+tUAfhpwP75Tj+u1rGMlswMU/rlpe/KePMsy046kLN0WlnhiXzGXPh0vmMAx9D+sbd/kxeXv7DAiEFReW2w9XVSjdbtfdzImXlsspIQZfRiNyRVW5iLC3UZaD09rRwBHpwVgiFHTAGIzCnV1smwZsG5SjEkSE0R+9HpV0oFB114lBFdTkPCRdJI4tJo5mMulv4G8ZeSixMtTAki4EIoQToMzRXueQyRaynPyC4thF7LyJOARIJjhNYZLCT772Qnhoqy5q6rWuS+1r1dKI0qS1YrD3piImC0rMPVN3aXvKwKd4y1qbDx4GYaJhG4rGQNvqwnp+o5VTdtZSsNR18nOcgUhBeXHwULe/8mJ47o/hukE3Oo+76hBm7hYvoLu6ix85Dpxby2GZ8Wb+HfAyo2ZJGz5z4EnkJnc8FeplAfAKXHEcd+IsvRKyIznseO90OmDAn5Uxeqfv51y1VPsBfY2fw/HmQy3L2zEXdmvx9E+duhPWWhw+8gLOnvkwAMbZeI7G3jTCXGx95q17Bi4v+JRoXbuV/o3xFImXqgZFyOTS+a90YZfiroqpZZdBvP92McfgNYkHd9rV0Ip5ChDFqadNxokHwdiWa8+gwYoaaAxkaw9IeRNyr2e/jnz9pEolHJrfdE1jnF+fQ5aL937LzDrO4JWIqyQlGwzQ2gKeub0CiHDv5SEWV5/C5aV7AWJMfyTH5rckO++hQzW+OT+Fe9fkYK9nlvu4b22ER8/JfhjLDR5aqUGOp55Z7uPRtW0vMz67vQ0CsLV5ZSPtqgqlyAv/2i3neoZla2E5LDaHMIPbDOeY3NoGMJljeLUePL2RubIPxmVmVWoBECHLc9R6UAxL6ReiqJQEyXRsnLC7kismi3hhUqnwjeOoUIbR+/rr2sTjPS/aoT6OPtey9+3JGu7H3t2Bt37cVUJvVRhyAeLaarFy8W6oMhIYGmsNgsB9pwv7apW5PjIgFpk3WCIFBrV6gzXTZjCZGcrcWjvO61pPEo2ja78Cc4SWw90rKFFiSHYfBcYVb1PHr70nxVoOG7uu4p6/FyiKInrN6BSyQFoUElrQmmmU55KM4gofqrVNCIURg0XtEHly7RJFehS28FR1BZ7yIUySlPEajR+ReA6N6lKUVGN9UdDAL8E7iuGNIIaaYy3ERk/MM/G8jueZXj3+rBBul7tKvTt9Jvs5qHNFN/4qH0k/nHi3gSe1ff553FZ4oa3K08J31galpVEHvST2JL2nHygWFGs0zi15wnuNSyehWzzlCeh5ygaZbRs0svSIuq5RVbVzCAzK0qAsh+5axnA0BBgYXiXPha4WD/vFn/gbrB0z0QAq8XRdYdwSEYFnW7NEBBwBmU56EmWSZUGZNFK8MctzdLpdjMoSTd14T8hXsdVKxpEVHoisqYhhUe1qMsVbOGhbemGORhNbXrlPw5CRCYvK8bO8nvKf68pGu0Fa3jpU4mVvbasSjGkrjBKUpGqB8WvHFx3lGWrtBVsxKCz5RCetD5ephxS9blMHPptNaalKOoSrqJ2J5ghkVCFELoZXhHpvDp6nlncHhwO+wlqKCJqmsdA1G2aLX/vN376ySfXvGF/43/4Bf+Ti7SACvv7WK34ePbQiwvsMvQyAkK+fxLV4qln8htDJGBzTnfKOpzI3DrXjKZPn6O4exGhUuj1aMU+FsWrx1NKmP6/lk4cq7zWcppdwRCv2EvzmTD/P4vphzL7qwkOHQ+kmf/3afKyJfG91nbGlUIIdGfHjOH+630drIzpfgr0YcS8pbZ3x4g3P8YrJ0bwk8hmWALWrZ0ejxmDASlFGIsLhhS18l1/UL8FgV8XYXbu0EXcTEkoXnjpNLzvHh1CsP+j6KO1cscFQP738ZMRTwLHVkzilFYzXHkRooNDj9PKTznBhwK3TkqNHtvZx1IvfACBGySP0U2Bm/NLFc/jJf/pP9uWpa2Z5KVEs1AtofysTXGa5ElguGreewke+JpezpGwjm6uquka33wczY1RWKEej1n1U8LQHef8JpZ+NC9lW2Eg/s8ql0T1EggGRYFIFSiC32OcYfMwdlue0S727D1slEby3EZWe9l6L877cz5x3IpY4kTuOWdcNCN61h/uV90yYfSn/oPz1r1hUqpSIWA9NbNPTXeqdOEZEC7Qtqeg3pH129CFVApGFxWBXGTYaS8iU8tWPdZ65+wn9xyzTRpWKWmhu3K+zfSgA8OyB1wEA9jzjhw/XEIEm36lIquYf93MtXz95FZ4iHOMTfnFZD51rGt2wWOOtqVNgBg6ViyjLd8lTixtglrUd3y4GmBhHced++2r3zHX9nY7qo2cz/NBh60+MlBAvB+MiWsCOvVcNbOh3JjIAdbwDeYIRFf+v8ysW+mytM3LY3de0qlo0YyFo/wUz7PxlYG3Btx2LG9pxnF+b9dbkytIGzNqs56l87QGp3OvegwC7tOHntQ6Nekr67KNacYAJaFS2AUQWHGVEHls96RSTvLfMOGJPgEj3vIVNqaeWnvBVyoVVjFekR/kEzjJgLn8cYGClAX6Z3pR2j5fWinCNfSjBKlWChrTV2IJUAgDjpy7qIMttGMZPE2F42zRSxdiVUJbMLkZVSQqjaswWM6mAGhPi7bbvkfHSnP1ccf8bavUsdn/hB1utOSfo3fXBC9AHX61tKmCFXt5BddKZotfaF/dL/5lOdKfyvFURL9iz3sJ7FRQdNeC+5PCo6HgR/9swdoGeXjk5hmjRUL0MJVlUZnwfSoTwgj5vv0HjcYMhMtOj57bnKrfaf71AoyJwBoQWSQUiuiIoVckuvhJPhTANlK9INwFXsk8HUkKnsdbxFMOY8Psr8pSfBFHbEcb1SmiPnxhFrU+ctNQIhwrk/cc9CoGC2vO6fZnwAMfzckxB7vM6vrd+R9Eap0Y4bMRT+nCODElvVEFfBCO2FYHzL+JB1pIzKlMCT4UhCOvW6iWpUmgTQukQ5gz5ENyeS/fhyX14KpL9KqmuJnevXnplrACkM1HC2/jhqvm9gR0EMGXGHWfJoCxeJCaU1QhlJScr5kVHKgi70xaNybzg1KwJJcZ+HbuW0NkXsRCLhCt5Bm0ze8z04UnBgtZ2abaT5nX7SaaXMkN1VQjxiJcQT8LWomhLUUQL/NaVbXAxX5Nl3irTGwk1GHt2jju5wgwf6/XPc38ZDDSiHGJ5ozW9dIw0l17CKAZZphsywzXjI+P7p1QmCoPQIvKYWHXcpYzRViaBsfa1Kt5DxAdDAfC1qp45fAfuO/sqDh8J1+o8KecfR7F2EiusyQfOmtcDnoocWNoCAzhDL6OsStzy/ftg2eL8Tc+gGcl+rnL6omTNbSy+C54iYH0evLjeVtKxMIT/ERAJ3XisPzU9E864IQLWgpiMPyf4qRj4A+1nZC4bTXkqNhaYIdmSsQB3Htu4Qo6jFP4eNDZHWULKTSM8lTme8o/UBXmwtGt9wdNkhVk8FhYZWi3oGSN3ujF0nO0MLlpf9G2IM8jgrLzGlZYnt4HZZARe2MAzy1MgItx3OsPTt5URT/U8De4zG4FvI+XHDBy99ABOH3giIgy8rDiFl0B8v//qfMY41KB1wN9+uLqHgkDk2MsQiWfGCiFyEJY+NuI+rxt3VG8O486qlqMkJZ7b7fdgsgy7O7suGwuu0GOcOebu5yRHWEQLmVwqjNgGN7q1FhBbXYRI6Lsd51EWUbwoTqBQJoRZNl+rlec2NYU9IpLZFgtpdhk2BEmnVkuksVLWXo5dVcsDznKNqhKQCw9m4XTJwHiB3sbIvhsQhRx+RzPj9nmopaGxZXIzKNiCgUzGZF5JUCA+AMDkYUOdFxBEMASYU5ZOkAAAIABJREFULPcp20SETlHAtjJ11Bo24WwcQ2FNSOmsFJXBASiUFjFkkLnvrdJIN9zVGqaJB/z6ADOw81wVBIb77B/bF/DnKFpKnaO/t85vgp0Cxdo82GV65VkOc2BbaGkZt1a34zX7HHo3j0BZhmbQoKkdT3Xk/np4k5ZYb/HU4jo0dMZLmyAYkAnGhnrolnUHOHD48PNyCiKF9FktqULrkRE0pnRiojiWknnhwpxkQignXhzfl6fgeAoRT0XP8WtvaBt9vroFtcUkOVpnB7aROWVjV+ekBYsbwOpctGYo88yvSS1tugINMoKP4KdABPwE/hXMDWp4acl5lgKUALLNJdDSpg/zmrUFAITMALS8IzxtDCjPUKDA/RsjYaXFEYC+56mFS9+AhoWNuQuPnc/BAJ4+fCvuPfOqn1WfnJ4CGTPGUwYEqV7Ni1s4vyGKs3HHQNM+G6FjXGMNhcOflvBwGtMb5qrlKbL4nRa2krmixCfAZW6J1ZQXBZghVUybxlunItCje+vt3b33LNr68irwv4t4Nli+FFzH9j0RFWcMwhVeaWj3yO2Mja7wbXINMoFWBG7FaX1f3MPZ0zOEE1W5KZPoZ2q1+XCBSHmngMIaV4ghy/eki9dw93AKy5/4aEL7mdkbCvF+Gu8duTZQqwaZU0ocGRXuWi3Xo3TSLCfLetBaWHtjy2CKTgKNPBqZzG0yeo809saAdn+vQ8x8tAARYfvZys+0X6UTuNd+Rza9AXKSHwAQ4UtLK/jC2lvwxoBlvN0xbt8QYNbncPPsZTSNxTcWOsiLk/iVjdtgG8Y/vGDwawcOwYeMCPil1QXflj08dXleP/Q8Bc86kTLQ9QUA57c/gqOHooPrCDBrC/7eUv2W8bHvdnDOMFZsnCgCmW/R3AYALG2K5b60AQJgVudClWrey1MqKh7d2sEnp6YiDzY8R+vttXgKjk+sZ3AfOmYwsDorfEEhKYIvzUp0wE0ydspEBfObazPQiMLK/CY+zy+6FpwA2cAnWNr07WEAZmHdy08COcWkG3TJ82/TyJrjufU5kCGsf4BQIPDU2uJ93iN5ighTi0Lvu599CRxlaj66vYOH6KQvhfTrB1ZgWL7/HL8QEVZ46i1XtHll73Y+j3d1prw2IB7AOG1Uh00nrd+d6i7PosUettaXQBcrJJMQlytFT9GgxkJanzQewYgX2Qgak4dbi40YgAJDwKefqvUf7idCNQjPWDn6hmjc3/N8pPTcP3H0LQ5X6eNVaO9d04FS0vXL5fRFzxPSqC8RJqjloFS98rGMTC1MdwPLgWnkBuSZ3ys4OA8wFuD6vzKbjjcB3ERzA2FuxLWS/II8EUjL4ESup3X98gyfmRYdw77kqN8IXpXSzx/2xuGa6xVxOMalZkTvvfF8ZZ4iUfx13aDhiKcso3GbZAG0eEpxNZ5SU6LFU8Fa88okvA+N9mNMXjdBF5gDX4/vTm+/98oraqyT/yJ8lS8pfEYQz6q9ZhL4XHlDi2fGYXqZenF9Oh0JCy2P5KMwzmWPE0Y8X6gh16KPck675lc8znovinjKNu3ZQGM8ZdnCsM6Da/MUWwvOSC1L4adYhvk54knu/+6tcrA/rq5Q3KCpjmcKhBJ30kRtd0Nq5H9bNwDDxR7hx35UlmhYYpKdXhfD4UjOYLCNKwjIPnVWlFCYIO1JqBskyb+Ox8m7yo2FyVQpuO/iuv6swxgmis5SjZ362GpLEMcLzQSCaancdugmTBRvjTj3NBaEPra6z4RrmnbGSXh0YF5RKs66dwdZyW8b327tD5zi1zbGi/n+qRHPtvviYNsiUPtvAF+LS0J78nzbWAwGAxdmkAVbpb/8nr1AEiM41ibsw2Ig40MeQZpQq4yGbI61YIwR870GEbaeq9zLaF6aDJRleKuQPVsGgad+euMtYHkb9uI0wECeZbi1IbyZi11x46jCN27uIMsz/MCOxXB9AxdPPSdhjPlZfH71zSCwlrfAi8EbuRJPPXN8BCLCPZcHUdtlvnxzsYv714e+7T+0FIwwuHbT4oafD/esCofw4i5usRacZfKcy/P6gyCMF6UaMK0uwETnrT99vMS933WbQpfW5dx39aSY8cyi1Keapx7yS1uBvsa4eQbw2oLMhiU5r8Suzjnjg+Szy/PiERGB3KmOdmHDC2nNOkTDaNbmfeXszGR4Mxc7k6NFfWbG2bUZrOhmRTDiIScgeF1KO8tXnLPfvmEaBMI9q7uwWuPQWsy+rGcuEYhyJ4Tkjt8/VuHe1YF8dxigqLAjM4PXrRwkRoRfti/hqUUpk/U093Df2hC3zh0FGcLbm2dwSy18Vc0f2rd9wDU9lFhIc/hD+p0TxhQV83MDnXU7jkBSQM66w65G5Uh2wFvGYFDKxkdIvB4QSwzMgI2PEhViqdOh6W++QQw5Pta1mbQ8KchvvFIrQGPtXoHA7cRn9n3wStIyGsgOe38EKFSGx8fsUrRArYPJXoiC1OoIYSXbWDAa2cgGjD1DqzFzMJ+AcA/3oPZhYPAehVeRRAA1rXRMfx/XbvVqlJaZO31RhbKayEZNPQjj2KaJys24MVO2YfhwWrCJldEsrIU/gVOubVztNvZ7erQ97Kceo1t0/LyzCEkA3ruKBZMaOLjOQISZjwpvUPTPQ3gDtNgBnw48dek2CQluP1fh8u3ToBvkFiu/8RSW7r8XN41kHW6nHOHAqQLGZHiNDG6pC3xqNnqknpJprStRonMDV+SprWdLAIRHkUXGjvv+XI1HqYAaP+OL2Qy09grN3t3x3zdNg/ve6IINwcTeKAHfur3GPa/LAvUzt1e45/UcuCQdsQsVvnlsCGbGn67KHpjPr77l5iBwzyX2z0YW0nlNlkH5ySuu1TnfTx/uW18AMvIJC4/u7OBTs9MwG4vekPRHDdtG5mnE8ys2hFnDoX7yu2xzyfET4yw1WLEaySGQqyoBFkn3Zo4WT313XXaqH5+bxj1rIgfPmlkXcpRefHH+Vvz0xpvIjBTg/eLiIdx96mVYy3hq9QP4M1cP7/Nrb7aNVWYUvQK0dcDz+31rYqj96tKt+PPMgCDLEDx/K/7uH3wZzIzlB648va+9KK8U2/MdQ0+M8/+4iRXvkWCjxehqMCyyPPOWsW3cEZ+xMlC31MXBNZ4Yewlaw2evtBh3351r6gYWsbD1NI2UBCJPxCtQbl+H4Br6aEIU3JewM/vnaRhJr40VUyyEvSutCoTDs6VJUZuhzkOgQSt6gKjP7m+r3TYokVggq7D293HKJHhR+jDsf50VwUXxNRSPS2iPlK8nX0eKETwTVWKBBsL4mjzRAoW/Xhm7kvb7zdv3GkHB7vOdm1PX4ikpppmh5los5Dxv81R0jlGbp0KY9d3wVCBfu8U6lsxhjo33I56QLZ7SvxbgaJ3I/y6iVBzS1OMX9H6sBhuAPD5fRqMHrtUtL9bfLbQrniaBNvrjuNPwPMqOXkSyrieL5e3sPe2XeunaTf8MfaYqKd/28Di0xgcurZ8901L0G89TiI3MaJ3TtSXmEyJdtxzrazwmbqxaNcxwZbyLjY1jN/APj4RFZIMaIldSWxWChFhkEdig1+86B6QBcwOTFZ5Gvh9EbnEoWPWtyW8J5GKBYohyRLS9HQilEdzgZ0E4hoNoXNIACRHZWw5uEKyVqp+uz+pZAbqQHy3ascQ3RRC4TCkn5Ch3ISeXJeZ1C8SC9ESPR9j1SyddHKbQCRvHbyWFWHdCs3PwCMRSUkMzpsJdnbVKxnlaId88pjsMuTCXbvIkP/F017pFoLX2JRZMWoNNUsMtOt1wVr0Wgoxj8OLJSVvquvaWndIoCF95b31Fa3iv73rCo2vH8Om5aRARDpx9BReOVeFLZnxp8RCICD+8fMZz1tzdXfzF1vuh/PD5H+jjG0d2MSpr1HWNvCiwePkpNLbBl+qfwBc23nlXPOVDxo6ntBgkM/DQ7IxXPCBJEvjS4oo2FJ+xX3KvgEf4s60Nq9ZaPLT4BggucrGHpwCQWwMwwUPX46RBhHteL/w1YOCe1wvPU/ebt/A/Lt0C9TigYVzsNXrimnOxYA9hVqgQAcA+0+yh6WloAoRvHwc+1qM3QHIKo+Ewb8EsITlAsqi03wRJGzaRzCTVVNLvFV+7TkK4K3OzztsLYmHFEp65rcS9b3TATPil1fPSZ/cc8/oGvm1vBrOUxvnltTf9uEt3Q5Xwuqr9JuFxGulDpcxOI0k0RPi15UN4+Arz+6qlVxISEhISEt4tri/zLSEhISHhryySQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAiSQklISEhImAjyq335Z//2v+bD3/9Z9Ptd5FkGABgOR9jZ2cXmxhYu3/Z7yDKDopOhqS2stThw6jM4ePAAsixDYy0GgxEuHHkEtmkAAF+d/nv4B2caGGNgDGF7e4ByNIK1jOmZKfT7XWSZgSHCYDBClmUgIgAsrwGACJkh93kb+3z0VwbMwH8/+BfY3t7BaFTi4E034B/VP4detwsG8L8ezrC7O8TffmEDF4/9Loq8AAhgbnDknZ9Dt1ugyMOQNo1FVVVo6gadbgdFId9tru/i9OmzePH553Hm5v8TB298Hw4evAlHjx7H9tYO5uZm8P71X0DTWKxf3sI777yDP93+b1B0DU6cuBN33HEHAIMDpx4GmNFYCyLAGAMi4J+9jzAajVAUOT73ToZLl9bxm3fNodMr8J+dqlFVNYgIhgxem/0N3HD2x5EVBv/3iXn8/deHADFWj38FTWnw2nfeABnGibuOIct6eGvrs1hd38Co3sAHbz+KmZkZgEksIyLUVYULb76FzcuraGqAuYef/emT182seOTF3+WZmT56/S4+vlECAEbDEf7b3aP4++vfgOUPw2QGRWHw52dfhnW0/Vv3LiLLMlhr8U9Ht6GpG3yufh4A45/P3If/0ryKLOKpP369gbUWr37oY/ivuq8jdzz11e80+PaxEzDGgJnxBfOK56nckGegmGDXA089vdS/4nf3Xh7gV+lDV/iWcf7cW9ja3sGHX3sBB288gDc++nH0el0AhLKqsbs7xO5gCGsZn6cOQEBGz2Jmeho9x1O/Snfil/gl2Mbiv6s/gF+snkO320Fe5Pg1uhNbGwOcPnUGLzz/An508M9x74FfxMGDN+HIkeP42neextzsDD5z9yx+xd6B9bUtvPO9d/Dc899G0TE4cafw1EMLp/HY+nEwA3effhkPHa6RGQMQMNwtMRqWKDo5pqf7WL20jv5UF51uB8YY/A/NB0FE+Dy/jMsXNmFyRlYYvHDTEsAWAOPR9eNoKoOPPvEoyDD++g8ug3EfvvLiN7G6Jjx172c/gJmZaXxsdTjGU2/jpdMlmgo4NF/ioz//uX1nxVU9FJMZWLao6wZEBAZgDCHPc3Q6XRiTgZlRjkpkJgMzoa5rjMoK1loYIhR5DmZG3TSoqhJ1XYNAMESQOzKAoByYGcwMaxl1Y9E0FswMIgK5q5l531l+PUz8vyx63T7yrIOmYmxv7aCqKjBYviSCMQZZZkBg5HkGYwij4QgAYK1FXdewbAHoWGWo6hrWWiEeGP3pLqZn+uj3piEaSZQZW+tob9HYBkWeIc9zGDKoqwZ5XoBIxrgsSxgCTEZgK4qrLGXSV1UFywwGgwkgIlhm2EaMDji5RQbodDro9fuYmpoWwyXPYRtGOSxRdAidTi6K0FhY2wAQ5mBrUdsGTdPIfJCegBuL9QuXwZbQ6U1jbvGGf8cjeHWYjIS+jqcAgDxPdQJPlZUIfQBV3aAsK7C1IOUpMOqmRlVVLZ5yHAICgsEV8VTTNGis9fypLBPz1PWmTP6y6HX7KIzw1NbWDqqqlvkOBJ4yBkSOp8hgNBRl31iLqq5FBiHwVB3zFAH9KcdT/SkABLYEtgCzBVt2NG+QFznyLAdRFngKgafIkOepusVTwtcMdvxDsFZ4iq3wOzxPFRFPdYSnrMjpokPodJWnhNeZLUAMZou62Z+nNi6sCk/1ZzC7ePCKtL6qh1I3I5y76bdQFB18YO0XXGcIWW4wMzOFrakeqqrCYGeI6ekCJjPY+OAfoXv6YRT5AjqdAt1OgTzPMRwMMRqO8HfPbsHOzsAYN/mJcOn474At48jFv4P//aho3P/0VIXMEMqqBFWE6ek+LDMMEUxmWpNeFc5fdRABn8t+HrwE2DmLX6n/L5gmg3X65Bde2cVwWKK0wNJ3H8alY78LZovl7/44dmeGsLsNwIy5uRn0ul0YMjCZwTsrj2Dlez8DWwgDZBmh1+thZnYO1jJmZ2dxw/Iy/mTmP8bDz7+DTi/H/3IP4wurOeYXprG9MwOzYTAzMwMiwtb2Nra3dzG/swVjDIaDIRgW79z6CJqK8WOjHn6/83fATPifb7LYncvwX5waoSgsRmWNf3G8g06ng//8beDo5Z8DpgGTAf/wfIUzR/81Nta2sXlhGwdutJh//a/DcoOtQ09gun8ATk2hHJUYDHZR5LlTwgUIQFPVWHv7Im66/Vb0ZvvIuvyejuk46rrETlOjLCtY6kF54AvZK2hmpvDE7Juoygon3lzD87c/gMY2eHD6NTxmaizOz6DTKfCj9jw++OYqHpvrYjQc4WTvFdg6AxvCY+cy3HuAhErMuO/cd2CPAYDwztw9XXzk6RdAIPyHdxR4arEPQ4TdF2rM39317WRm3Ls2fK/ItAf3Xh741+PeytNLfXz+8otX/vFBgG8AmmMfwG9/YxVfoFfQsz0wCINRiVFZomxKNE2DPzmVgdli+q4epvoEaysAjAemXsZT3S6IDD7Np/EkF/jkqIEtLD5Xv4Bfzz+EXq+H2dk5fPnSZ/HXFs7gpoMWi3MGOXXQ1BbD4RB/7eaz+Fqzgq2daRhjMDM7g8/jZRzcXMXO27v4yPIGjDH42naFP3qV8eCNIzQV48WbF1EUPZgsQ2aA3YUOpqZ6KDoFqrLGJ/g76HQLdAcFsGjwreU+TAYUucFoYLGzNcDhbz6JM/ffj9fWVmG5wX1bFtP9b+PpIx/A6voG3v/iX+DE26uYmy1hHU/9Ot2J3WYH9ORf4Kc/3UVvtkDWza5I6qsqlLVLG1haXkKnKLC5vYOiyNDUDd45/AgOvfXTyHIDoMDUgR5GZQkwI8sM3rzli+hf/nsgEgv0ljf/Fs4c/JcAA79xxww+904OMoS6blDXYmGxZVw8/K/xn5z6KRhDYGYMRyWKXLSpIdPyVAB4LfrvI8gAWU4gMehBROh3C3TyDI3tIs8M5i78bewOBtgabWLt9t9Dt9sVJf7dh9HJOzA5YIiQZQZwFo3SsOgUaO57FNlzBW793s/gSPcoTuYZLs5PY1QO8SOb/weAf4TVy6u4eOn7GFW76PemccMbP4mZC/NYu/FfYXe7BgCcO/hFVPUAh1/7OUxNTYGowOCWAfLcoN/voR7VGA2Aqqrx6zfU6O42IEMAFchywunl30JV1fjD/s/hF7/3U7ixbDDYLXFq4zfx1s1fQlUPsfDWNBYWLQbZNkANhmWNy2ubABn0uj1UTQ1uGGVZozAF1i+8A6znaPIO8PHb39OxjLG26niq08HvPzdEUeRoaovenQWm+j1ku0Ogk+PM7Tfho0+9BGbGgBmDwRD9H+qBCCg6HXS7BbqFmNndTgc5SHiqsajrClXdABawtcW3lmc8T63+xTqKPEde5Pj28jR2vj2CMWKlAy5CAJlzzyz1sf1c5ds+89Fi3z5tP3vta94t4nvFuNZ9rxUSAyKecgIkI8JUt0DX8VSWGcxdrjEYDFCWI5iBEZ4qCowGNTp5B3kOMaozg2eWe+h0CtyzuouGLYpOBwuLC/j05jn0+scwOzeNmbk+fuyuEUblEL//Yo2fX9hFc+rbOHfuPL7meMpgB6PdCtUusNsVntpYK1FVA/zp7jSmpqYwt9LFYLDreKorPGXEc9na3EW31wEZgCnDswensLO+g6qs0e0XmJrpIy8WYN+Xo//Cizg/KFHWA/z2k8DCAuFOexm7ZYWLoxq/8/wOlhYLHPv0Iu5dG2HENcqyxozJ8e2LfwpsZDic3Y1DP7g/ra+qULJM4odZlqFpGmTGODerEYITgXIDy85FchEsAqGqG2RlBSIDgMCQMErsSRARRqMS1lpkRsMrhKZpxP0DkOcZ8ixD3TQS6qHgyksEh0U4/XsEIkioKP7MfZ5lxguATifHqDTOhWd/pckMAFUeegf5XjxDCbH0ul0JlRjxZIwxyIsMu7sNtjZ3MMAQ21s72N0Rhpybm0NvNAVjJI7b2ZI1nKnpPjY3KjQ1UFUNOp0cVVkBNgNNAd1eD2QqsAWKouPXxZjZhcbg2pbBZAZ5DhSdDN1eB73uNGzD2NkeALSGemYEogzMQGMZTWPRWIYh6S8TodOd/v+4e+8oy7KzvPu3w4n3Vt0KXdU5d09PntEEJKGMAAUkIQlpJCEkQBkF4DPJ9rK91sda9rLhWw7YBAECDMiyhJBGYGTZskAICYnJeaZnuns6h6queMNJ++z9/bHPvVU9dM9IgJmw/5jpUFV97z3nOXu/z/s8z4vJVzB5QUX///bl+o7WhZiyaOnpO1sLysI/3AXK0xvOeWqmYaOqqkapdZhynka5OKbECFNiHaZAoNZhakifjNgx8P/gcxBTIxCxhi0hPLUvRpiCslRkVUUUNxVb0x8cVn1rP6ehhaTwVFigiJPI9wcbTKkLMJWTZTndIaZEg6m4i5SatKUIgwZTqWJ1pcIah6kMCOcx5RQijYliXy05B0EQXICp4X2zniJ3GoJQE0YBcdyi7jv63QGwjIoShGwwVa9hytohPgVh1MblNSYvyN3SJT/nJ91QkjQlCEOUkizs/RO2HH8LOJh89PUc3/VJUtFCSkW310epsHlAWKQUnNvxxwRBwOaTb0Fp5U9NVcH7D5e4JEEiUEKQZTl1bQnDkDAK+K39IXle8Kref2f76XcQaI1UkjwvUCq6gNT9zb0hAB86XF749H0uLgGu9k8WIQS1tSilUEpxbufnmJITTB3+QV/RxWuUhxCC6cNvhAi8sMH3tIJAsfXULSD+atiYAnxFU1WG1dWMZbfK0ZlPcs7OoU4F7F9+D4Prv0yJZO+ZHyEY8w+ry1bezUPx7zK/6wsI6dh1/ocxZYXA8b7HCv7rFWOsXv4pTG1pqbcDzvcKrOdw/SbpKTChPVCFgF3n30F3/PewtWS5dwpTW4KgJErbCCFRUiGEoq4dyOb1K0U6NslgKcfkJbbsPU0X7OIrTVOCMEApydh1Id8cHMAYwwvtQ/T7OWmrhdSSbrdP+3qPqdW7CqSUvj9Z+M7jbRMhS395nmyQ0fnece6YTFBKoo7m/M+DhqkbE+I45vulxC1l5HnOnz1c0kpSvm+PQCpHdm6VZEeEUo67p9O1/iQgmjLlUpXBegqKHWu/vIOAmxazJ60YLvYz7phK/Pdd/+193/Drn7ie+O9eP78P1P2j3798h+HOKf/Z3LRcYBtMSeF7W9+zG3o9x1+dvI0pO8Ge7rsIAk0cBwT9AK0UUgj+Qt7VQMaBuJ6f4z66qs9JvYk/WL6TO6+5isXJmM1TCXsWVxtMDVhZWWVSnKMKz6Nkm6mpKe4410cgec3lCh34Q/Ubrruaz93+DV68pUDIAe0F+MLdfYIwYOx7WrTHU8qi9phqt7hxaUBQlbg4ZOWunGJQ4xxMviQaiZpevtPw1WMpvdUpbC1Y6p6iqi2vvupqorTNuXADGzdOMTHR4US94BkN4TF19dZt/J79EfK8y8eKv7nktXnSDaW2JVVVoLUkTSKCQOFwLO2np0/3AAAgAElEQVT8M8qeAwqc8zvdxESI0BJT1yzPL7JxdpYoSuhe8UW6vT795QFlXiBF01SWgtpCVRUIQEmFqQ3fN/gdTFVjnaCVJqNGcxgG6Ia6GZblHzhUAv5U+pzcT57QF1JaNQ3WtSZqHIYcWHkP89Of5+z2z4Jz5HnG1YMPkiQS56RXBzlHVRlMXfPx3SF5pnjLnEI4QVkV5EWGtTWVqQiCgN1n382dG/8zplcipSOOQqamOmw8/06MscRJgGqqHescVxfvoSprirJitbfMh3vgXM6/3VvRyRWbTr0JgeRHlpZYvOxWYhHxG3t+nPc9Ztly6q0IBAReGCCVL+uDSHPl3Ls5vXCGvxj8IldedTUmHcMASRKzYWqKsXaKFDW6qW6iIGXfjftZPDXD4ukzzB878rRcukstj6nSYyqN+d7WMarKkOeaLCuhX+Ccpd8bMNGZQCjBXXsOcGXxTQQJUagJwoA/PbGJ/f0TvqppMCWlwOIwVQH4DcYYw2Aw8Kdcp0jTmNsnfVP/u6zlnpnW6GQLnl2ANdrrydalNo1vZzO52Nc9FW01/Ptv9+dfdAnBjYsZceyouQ6t7x+d6EXTo43CkA0bpjxmyoqqLHnw6EHUd0+RJBHPWziA0o14ojKY6g4G1dXkRYFSywgnuObsCjvjReY3bvSY0gGvv6LFXxz5EtvL/QjpiOOQN+9Z4KbpmxtMPdJUqAKlH+JtL+xQlV6QsbqyzPfuEzhK3GMn6ExM8ODWKQI0K4ur5PkAhKfgXrHTcXvHC26E8JhSShInEa+/TvP5apzaDjh6vMvrvvv5tIaYimM2TE8z1k44Jea4d0NKuKqIgpSXvOKV/J+zSyyeOYN6/MFLfrxPuqEEQUhdW/KiIHKOszs/S54XDPoD4thzjs464jhFSkVZ5vQHA6qyJs9LwrAiSRPSJCHrD6jKiiBQoxJ8kOec2/7HbAgmSZKIqjJUlZcUbz/1Vk4d+AwbDr8RhGB5762jsm7D4Td66mutm/KcaMo/cQmxpn5boyT8TaLEkPPWJGlEUWS0WilJkqKUYm72c4RhyObjP4SSYqTm+J3L23zwcElRWFbDEKU0p7f+EWzczGz2syzt/xNMZTGlRZ4MqEyOMTU6CKhrCxiq0vCftzqKIkNrxc/MRSRJzK/tVPS7NW+72xJFAZUxDPoF/7I/TRAEVFXNiY2fJjYageDDx2oKV7PaXSUbZJzZ9kfsPPcjtNst2mMtPr474Jb5NX6/qntYW4IMEALKqqSoFCEBUlqwvsLSkWZ8dowiW2H+xKUbiE/HWsNUSeQcUkqKPCcbZMRxyF919+Os46qD9/C1K/ZTlDmDfp8vL+3k1UfmKcYFW1+e8totpzn2UDDC1M1LObWt6c9mPPqIoXjQEnck1Y6KLx8GKUNee7lE6QHMNVWuElw71236koI7pxNEgyrBxZWUw/X3eqj/HdZT/XtP9vemvgaAfy9r3s1fcud06kUr6hA3LYLn/UA2mAqc5srkBu5c+jpx+iBJEvPAbEirtpSl4a6pg3ztzD5e1jmEdRZrQUiJatSvSmkeKe9mtX+WV57aQiYlSZrwn4IbePEphQlPYUzNL6iDXH9+F07djTWGr4iI1pmvo7Xiy5338s+ClLooyPMBR84/wBc27aEyJa8Z/A53b/ln/NPuo5iq5sTR7Xxl+THaY5qtL29TFAWry8tkg4yqrGi127THWtwz20IguGv3Ts5Ek+j776UyPb528l6cCLhmfDO3dx6k3U4Ja4+pl3YeQwpB7CrGN15GnqXU7vpLftZPvqFojXNgTI2UhkBJTGUoi5JWKx1x9EmSIKTAObC1Q0pNXXvZr7XWVxdaNTSFp2PquqYoSkg9BxgGmt7Al7BKKcJQj6R5UvqNywqLQIwkjyP+t3m963v0z4X95WJvYfi+/KnS91TiKKQ2dXPyVVhb0x8MKMti9H/nHEJKnGs1MlXlZapKUxlDUZTU0hEEAYEWRKFDqQBTeWmqbu4F0Ui8rbXkWYHSkspIEhE33DJ4FaPAWiiLGt3SjUfFIoVCCg34Q0WWDSjyjLLIKfKSrF+gpG7eR4jS0t87SIwpEc4gCABHbX3Jr7W/9r5Ed6jAoROJDATGmn+sy/VtLa3WYUoIgkaCWhYlaZoC/hon8TpMWYdUetQzGmJKaT3i772MuMEUgiDwYpaq8kotpRRBqCjLyktkpcRZsMI1PhSHs03PbT141utenguYEn/7bYz+rMGUVpLoAkxJbBDQ7/cpS00UhRRFRlFkvo+Bp8G0ugimauevhRa0owSlgubgfDFMuRGmTMtAyKhf4xpMOesoy9r3m4eYkhIplH821jX5YECR55RFQVkYpCxQSqO0Io4iT5Vr7TFVFzgbIPQQU/aimHLSoWOJCqF6Ekw96YaidSNZdVCVNTL2vhRb1wgkg6yPEDA1MUlRFkRRjFYBcZKilOf5V1e7dDrjaze+cQ0XXHF662dI8xZR7OVwWb9g9/kf8aW6s0wfehM61Cgl+bx8Ox856jX0c7s+x6ZjP+TLw3Wn+OfaEkKOTowXW8MeSRSFzB75IR6d/AT9fpckSUBatFacDv89Y4deTRxHtFotPnAkRuqQIFCkib+5bOP36V39P9l94p1eVFEZTkb/nqIckBV9kiQmDAJarRQpBf+iByurUBlDLR1FUfDhowpTaX5xXwuhocwt9A117VVdQaC4pnw/9QDM2ZIls8T98a+xz72HmZlN7Ap+geV0wOrSKg+3fpf3PvaTqHZMe6yNWokwRqCtQunAKwpFgJYhUnjeedjkz6scM8jp9pdY6c3/o16zp1ojTAFVZZFSYa1j5a6cye8VfLDn/Q9TB27gHpsTRzGBCnj15rMoOYZSktWVVToTHV6y40q6Kz1WXE5lDGVR0e8fIE3vY8Pzx0lbCd0HNvPaA0dRSvoK0wmU0mglsTh0Y5a8rRMR0DSYn9OYEnx1eS9RFPE904//rb8fYiqO/H119vQ5ev0uJ7ZOcf3CbpQWPLL1CNO338unDlxHq5Xygg3bCBQEgSJJL8RUFIWEoQIEbRMTRZpDxxS9gaJ71TivDgLSBlOvBf7Vxo9QVRU/V91PXUVofT3tdoqSAR/NocwFhxZe4r0iwhIEih07z3H26lmMLVlaXOL0qdNMTk4xM7ORVrvN8nmPqSNfmuPA1Tv4lzsPMdde4F+LiNl4F6dWz6KE31CUCNAiaA5+gtW7SxyO/yUd2UyXbm+Jld75S36+T7qhjI21KIrKu62t30hsXTf0ST1SJFSmIs9Lb+JxfsPAyVHFcH5+kbLwPGIxMJzfeyu1tURliACKssA6y+aTbyHuBNTOcnrLZ/li/C4+dsKruD5wuOTYts8ghOAL7h18BG/mWbM7PvcAYG29Zma6yBpy31Vl2DAzzVj+U1SiQhSC1ZUuVVkxt+tznJ79AnmRYYoKeUrwXfKnmJycJo1aBDqkqgxFkbPhyBuo5VqKwcbjP8TB8hfJi5wkjfi1XYL/54w/AT/S+QTTp97cGPJiirwiiSVxGvBPVyJ+dYfEphE/uxghWt4V7xVmNQtLcwz6GWe2f5Ybyp8mnPFepTBSbNjcRocSTryLg/XjBC/+OnlS0l4aY3y8gwkjbHO9o1AThwFaB14d6CxllnP6scMU2SplWTO5c9c/4hV76jU23vJGNeM3WmvNGqZszUObjwKCOD7DR04ujVQ73zAKlKdkuvdW/O8dO7nu0L1Ya9ldbqbIC4+pKGTfZQ+RlZNYVxOGMzyweRzrLJed2M7BbSeIwgApBWVpOP83PYQQTNzksAydzr7ieaZ3Ji9Fc10/v++if/4z7gHmreWf1PcT2Yh7aP+tn/fVpT0+YcIYXjt7O1vGdlKVFaF8mOkyoaoqrj9teUS0mH7gfoyp+MbkLpJNgqmJKfbOXMle3sWZ6o8oipw7p2NuXs59VagEn5vZzYHzv02PFaaWO3xr/CW8tKkqz57bwqvU76K15t7NL+baud3EiSNKAq7f7yirw9jaMj75XbzSegOmkII7p0LkwS9S9TNMadh57esJgjVMTW9uowPJdbsP8JWHv8mfd15E7qZ43RUP8/je84QPRVjnuHfDEfYG24mjNUzdufNyiiznBY/9Gw6deQ1lafj9vTt42yWuyZM35esaa70kWApBVZbUtUUpTVkUGLsmS/VNPQHCUdeGuvZRKVKGRGFMWXiXfN2AZ/iwNJWhlF4GGccRZVXhoCkTFdaWuNpTZOBlr14+bL2krVGVDTnf59LBaiQFvoTdZuh+ds4RhsEolcA2CrC6rnEbN5D2BvQHfYrCq4UWj62Ak4SzAUEQUNcFRVFgaoNyTdSNACVlI021CCHJsozewHuC1KQmSWJ0oJu4F4UK9MhJX1V+wwsCRbfXJYljlFJ0u6v0ej0EklbapurVaKVH5wGlJWk7wlQ1i4+fxS4uU5kKZI3AIoRb93k4HP5w41irUKoagrhNPB4wlbT+L1+l72zVowPZEFP+sKakoixLart2sf3lX8OUbTBlrWgOc97LVZshpjyuPC1dAYI4iZq0BU9he0zZRh66DlNNbM5QqSnkUG13EY7oWbyGgp5LY4oRhsIwoK1aOOcYc20m5cTIRb5gPAVWlAWdsQnOzR9HWIGZLBtMmRGmbG1HTIr8W5jK6eeiEVasYaoKNWEUjqwUxhiqxkkfBIputwt2gFSKxbqL7PahwVRR1o0b378nrSXpWMT4ZEr/gZylpWWqymMKLAK57hrbCzHlf0dVC4K4RTweMhVfGlNPuqHkeeGjTwCtFNnAbyhaa7Lc+0fAR0NEQdhotWWjzPJcrdaSyYmULMspy4r5XZ8H52NCqqqiLEpAolRAHIUcmf0DtNLsOPHDfFCWlFVFVdeAYPaoPxF/WNcj6SyAEMpvJt/ePfWsWRe759f3iZxtPAzW01s68nJUU1viNEIKwZbyg/ynXY5XdX+XsqiIgoT7ep/i/HjKeP5RwjCg1+/7XkaREwaR1+ZL6VVlzQuRUvPD965w7Kr/RRAE7Dj7w7Q6MUJKalMThP4GNlXNf9kmeOd9i2gtMWnKysoSY2NjKKU4e/Y0C3v/jInxCQ6svJ9/vWGFn12S6ECPNsIkDVAq5fb6C8zc/lqkKagn/pCqLnC2BqlxQG1r6to0/hPlZa9SEKRjjE20SMdSWp32RT7Fp2/led7ECfl+SpZl2Nrysl3X8h/P1Ly0cwjAe0QY+h8EzpZUxsexWKN57dYzHHtEUlY+5sg2D8Gq6XGWpUHpgEe2HidbzdFaEYWhFzOUVfPzBRM3xmitmx4nI0wr0fgaeObvJ19d2svLJw8/5dfdOZVwrkwY7yS+l0BT5bjhz9njvRfW8bLOIZb1zcSrlbctdF7M1Pk+oumVTE7nPHKyQ1lU7Jrey2N//TDOQbZ1QBiG1IUhywaURU5lvDv5n8j7+ar6MncBOFiefj79fsZy72qChk42O16OlZLA1Dy28yTXn99HVdX0Bwf48mN3oLTk+y4rWVle5o7xCqUtZ0+fYSovmBifQO57NWe+fIqtr5i+AFNxGjAxneIqwdLCMqUpMDbD1AUv3HIliIg/55vUzo4wdcNSyZepcULwp+l76Ux6TP2rsaOX/IyfPHrF+NN/oDRxGpPn2agEl0JSlRXOQZwkOGfROiCMYkIdUVUleZ6xurzKxEQHqSCMQlSgfFOqrKkKQ3tsjK2n3opUkvlsgenVN6IDzekDn0bd+XKk9CfgT147wYeP1Y1e3J/YvA9DjkxHz7U1VDetX845fnNPwAcf99k+tbNNxpr/WluvfQ6VMWRZzgcOa47MWGpnCFuwdfMWqqrkzOPniNsJy6sLrK4sc9w8Tu/q/02aJmw79cMkrZioTIiKgjiO0TZi84lbCCNN3A6btANDrzvwQXVhgNKKf9Eb56dm5+kPlvng4ycxpWNpYRklNed2fY5XyH9BUsfYxPLO5M8Ie2/3D0rrUMp5U5Vz3Cx/mr/e/O9YWJqjKFZZWBDEqkfSCkYPu9IYjMnRQYDUGqU1Wy7b7tkh6yjzZ058CDSYEpIgUBdg6v7ZQ3yvEFx2fDcArfEWv6wtcRjx/RtP8aozXf6tuow8y3iNfdDL7RWEUUD2oKXceBVlUVGVOe2xMeRYilSSrY9MsDvciNaaQhfM3j/G/bNHCQJNmiZNdpXg2rm9vkJVCi0lD2x6/BmPqa8u7b3or9drkP6jdvy0EaOvmV/t8PrOuQt+zvCZ5n/tD2h/vrSHV208gamvQVjBjYv3YQHTYEopTe0sBsNCfYytW7ZSVSV3HbydRXMeFm5msGjp3vtHfLN4JUmSkCYpV7Zu5MHoAaKiIIhjtIoIg4gw1sRxiJSCyhj2HdlCe+xRCO8hjhSzG6/k5vku/UGXrx3/IlXpCJdDpFK0zz+f73vFG0iSmDqv6L5gosmFk42gw2+SSt7Hu17xGqqvfJ7zS3N8sV7l9UmHwWSPtB1w/fn9LO/oU5oaUxX8ZRyQdQ0q0Gw5sI13lN62UOYFl6pRnnRDkRJsXWOc9UoA4U+jRVESxSGB1ojhg8w6RAMUAehQoLRXHWSDjH6/T57l9HspcZygpPTu3argxOZPeed2HBMfeSPGGPr9jE1Ja+SU/9DjFUKpkUtcSj16iNa15/2f8Uep73BdLFpGCPjAkWq99XekhBsqv5r8OH/KDAQIwZaTbyXPC+b1Z9ARZGXBwYnfRlp/sh8MMv46/mWmjm9gfLxDf/wTrNIlz73wQklBZ6zNr+9UFEXBP1+pR+qUJIk97aK8Yz3LB7zvcMagX2ClpL7hL7GNsuympZ/GVIae6fObewN+7ME3U0eeIouiAGP8vQSOT904xvMem6HIS+ZPnaE1NkZg66Ekh7woGu9JgJAanKCuDWVVEjSfxzPtlvCYMhhjcTb0lbapKYuSMA75tUQihORnnTeyCjymwiggIUIpQavdInmwZtAfkOc5vV4fuUmOaM6yKth/YitaK+I4BprQ1qIgTVu+ydscxq47vx81DByVmvtnjyAYqjXlM5pCHlYl6zeTJ67hZjJcL+schif0Tob308smjvCV87sAX/0rKbl39jA4uGHBf6kUijAQnFjtEEUDnLN0e12PqaJgaXEBqRTVxLfIsh7fmn8l4bFHGe90GGuPMTH5dQ4e3EtVVVx2taTTaSMyQW+1z8GtJwlqT3ElaUxZXEWgDiKEw1R/Q2mvpKr7WPMAk52JkVrTXr8Vk3tMOefQaURtLsSUsz4Db2LyUY487yWcOXOKqW98nts6D/I9dnoUlJmXBVIJoiBESO9Tq42hrCx/2GDqZ2t7yc/7KXsodV0jrOd6fZRy42mwwx6JHPEwnr+tGhNdgalLhPQldlWW3jdQlKStFqKW/kRkJaKJoggC3RhxfPR6EAQEgWq4/DVfhr/J17KH6tpzvg3ru3aPPEfWhdtKYzxbl2vmGt59/X9xvsPgrMNimzTTguXlFSTKV4+rq7RanUZIUdNb7ZFEbaTwUtblpWXKqvByb6W82TAQlIXPEBJSeIl3FJAP/E1W15Zut+eVgFIipSJotTGmQmvFxESnSUcwlIVDSnx/rarA+f7cKFJHKdqtlCRNMZXzjWy7JlIwxvf4lIqxzhu4rHPgLE4IJM88tZLHlEVYS1WWTe/C07fOuuawtFaZrmHKUNVrmKqqBlNl4ZOfpRzl41lrfW6VVARB4LPynE/OTYOAMAwa3Pr+iZAeU7LBlG0wJUfxK8+sz/Dvuy5yTPM92GGsCmvm6TWb5/Ab11Kbh5jq9Vc9pooBq6tdWu3xEaay1T7JIEIKjakMdb1AWUVNz0QRpxFBEDQYME2PWBGGAZnx7IMdYcpTzEoFpOswlUx2sKeWPFVlLbIdUZYlxnhMSeUf89bWCMUoFbkeYkqux5S3aiitsE6sYQqLQ4yMl5daT7qh9PuDUSOpNl1arRZJHGOqGqnUKLFD4HXuWZ7R7a0ipeBTjy9RVyWvn1F0xqd8dLmpcbb2+nhjyPOCNE7Yt/CjtFspvXMDClsilWTb6bfzWwcCPvR4jRB+89JKjU5Mv75b8d5Hsyas1YPDiSYyorn2z7BnyT/octA8CATzez5PfObtzaYqEA6ss5RlST/LcA4Ojv82y9UyvWNdoiBhMBiwutJHBxG29k28qakNjLU6VKZgbu4sWa/0AXNxhNYRQaD5+QVNVSmWi1V+e39MFIV89KQjDH1DvixLfjFe4V2l19m3Wi1MFNB68NWe67+s5r9e0aYoSn7sgVVcFHJk6g+oypLt595Op9Np/BeOt9/d49x2S3s8ohVPUJVNkOgwr2jd52FMha39Jttut3wq9TNQ/trvZ/51Nb2nVpoSR26EqZdPHvGV/mrJz3Atpp9TPTLBkj7JC498FVNVrEYxl295Hi/cPE6WZTyanyeJH8a5nLyYZnX78zk+c55WK+XyU4exdUkYCB7c1OL5qwXWti/EFHDvhkNceXY3V5zagXNw/+yRUYbY0+FFuX5+H/fMHPq2vvaJ/ZN7nvBzhuunjeDc+X0cGT97wb8hgL9Y3svLJg77+0bCx0rHo3XN8xb2AwIvcbiHsiwZZBmL6pucL8+z3F1mdnUHYZCQln06YomV9C6OPngVZVlw85ajTFzZpTIFDz2yG1evsmXzWaI4Yrbb4wXd69HTR5kYN0QndzLWeowoConih6jrA9T2WrKy5MRxSV5maK1pt8c81ZnnFKVh0+Jt3L/xRoqiIj3zdW5Yeh7n57dRFhULnVspd78SIQX93mVUpuLntOF8mvCnB44TnzuOmX0xXjJ8P47Z5hr7DLEPZ340wicmEs8OIYgJL3ktntyHEujRqcmXzr7nL6UP8vNuU19pxHGINgFFUdFdWcYUlto4qtzg2gKtAqLIEehgpEZxOL4y9X72LBbMLywyyDJ6V/xPoigkTmJ+4tAPopQC4XdOKSWiUWF86LDFSr/BDAPwnmvrUm9pSHaBP2HOHn0zMl47OTgchzf8AThHFEcsLC2TnS8xDm5PtvDz+r1ktuBsfZ47Fn6Jssz9UKdQM8h6tMfazMzOUPQrjp04jHV+NkptapRUgOO/bIOP3LeIdZZ/vs3x0QVvei2Kgg+sZkxu3YS1cHTmk4glR/uaL9Fut1GLu9GBwuE5YyGdT0oVgnM7P4c59AaQPmI/bbUIo4h2u8XUZIeVYgFj6pFUepANEAq0DmjHbcLAm7eEEBRl6cUkF+lDPZ1LN44x2aQkI8AJy03LGUEYef+DAqMVafwYxlj+TbWf1xyNqIo+1jhmWltxFu/5ihwv33Qt1p708zbkfbzC7YS5LrVZ4lxvn39ARRFxcgK6V6LUEYTwPba7px/zL6x23D9zGNuYlSO91qd6NjTmv531H7Xjh5/kjQjgFZNHfBBtbxvPW9g/eq4461hdXQXniOOYwXJOlntMTW2aYeumTWT9krOn5/nz4w+wLd6CbLcJwgUeeWQP7bExdu8aZ+P00ghTl6lr/SG7wVRRFCjRp9fv+l+rmRGmhKrZsm0Ttoa5gWVxaZF2e8xjSil06GOx4jhECkcYh57qLt6Ke9iBhGW9SJq2CKOQ9lg6wlTdYMpUFYMsQyovGGknbU7snUdpRbKyl6KocI3681LrKZzyAWVZ4pxrJL3lSC+fJPFoAmBta6qyQipNEkfUJkXpHOccURQ0Q32MH5DURGZUlY+79xMFvfNyrN2iisLGra0JGxkqApzzM1B8AqZtptPJ0QWvm+FD8jm0saxvoQxVcUoplFQN8P17XX8St81AIC/LdoiypN/tYqoc6SpMmVG6DK0DpqYmqBYM1t9vmMrgbIWS47TbbQbd+YY+EQRRMBo2ZK0ljiMcA2ztsNZvcVVlyLKCPC+81BefMm2NV2OVZc7i4nn6mzojpV8YaNrj7ZEruWqSVa1WDPoZ3dUu/X4PUxeMGkR4gFvn4zKiMEQ3PQElvfn2yQvzp28FgU9idjjixFMTfkjSEzFlG0wpkjgmbaUM+l1wEMZxo3DzRl+PKe8Xo9lsh2zV2Fir6cPo0SiIoaJLuoY2tsNQV9m4rv1GYu2a3PW5si7AlLM+C62ZRrueRpaNwdM11b6pngJTVY4ONFOTk1SHjZ/dhMdUWeYo5THln4MeU2GDKXEBpnz/ylkB6gmYqi6OqaXFjH7QA+fbESpUtMfa1KmlKgtWllY9pqxi4HwayXpMDat914hhhPCx/esxNUzieSpUPemGEgbaR147RxSF625+S5rGgOeEBYKiLEhiRZzEfrBL1PPhhWmCxYcOVlWFc34nLssKieAHst/H2luIoojJiTGyxA/8mX38zch1swuE8PHdXiLZTK5Ta/zzx/cEfOhIBc3N8RzCwLoTeUYUhsRxMmrE42Bh3xfYevKWxl9Q86vbBa/JHLY2ZKbic6cLrLHYGozpcmz/f6XdmiAM27gvydFNkhc5SnrhQxQFdHsrVKYkjCKiKMLmll/fpXBOkoqAT+x3VKUhsA4hHef3/Andbg9324s4vvm/+Yd7ZfxDriyw1vDI2Md5x73vIdARTmkCHbJ7/p0ADAYD7tv7Gz5SRmvquqJaKel2V8mLPk5eeFYWSLTSpEmKdKIRJdhG998w488woVIYBI06coipAmctXz0q2Vy3ePmOpm8JPFLdTaIS4vgmvrTzSr57ZYlKCB7dcQaH5ew3lqmqks35Fl4WXENRlHzt+N9ww+yd9Dd+N1EU8Up3gjwvkULyXcsaGT3MC1b9a7lzOqGq/KbkGkypBlOeRXAjTh/4Ry1Tnkh3PdGs+O3SYZda1jlu7zxM1mDqZRPeNCibfu1/0o5XbzjENXN7RtH/lbma2tbkpWPQ+xOcKVBUPDL4FunST9Jqd2h1xjh29Da+e0sXpQWPpVfxgk2XsWXrVrZt28b/euSPR5h6YPMESxMRNy8XOAeP7TzF5cclNQ6tfVPcIbG149HqDzhfdpoD04WYKssezyvnCHSEVgZNsUoAACAASURBVFdw32yHIPK03mXHtrC63MU5AVZSFXdwZD4eYWqbfdsFB4abFgumXMzWBO6OJTct5QgB19X3UTcHNef+jhUK+HGjdW1ZXl4lDAPCMKIqvVmnP/CSx7F2i4mJDmVZkGUDpBS02mPe/GgKJqem6fcH9Ps9ED6vBgedTgetA3pXfpEyCukJwcyRNyGE4Ff3SX7i8WamMo6yLMnyEq21nwKpZJPZX/MbuzUfPWIuKrN9Vq/maZjnOb9U/DbvXnodExOTBEFEZQyB1p5uvO/7+Q9XVryZz2Cd4/WlIauqhq5UvGk24H/MGSw1sY65bPH9pOU4SgVMTdzBcvc8RZmxsrLCZfsPoGTA2ZPzzJ07RW0d0di45/pdyE+e8tWIMTUrK35E6cf3aI5v/kNarTbjnTEO979GSkRZ5KyurjLeaTH7+FsIdMAg67Nh/4w/eAi/eWVZRlGUVKZkX+896EARRQG/f/U4r5j7BNg5XD2HE01cuXCeotPe0SukIFR6FIdf1zWqaUQ6d2lFytOzBFp5f8Dy8gphEPoZMUHYNF+9UidJXsDXJ19EWRX8wOD3uFW+bYSp5DHLlTtu5LPiK/R7PV687QBFVuIc/MBVL2Bef4EoDgkjTbaUY9111E5wx/ghf+psZPZllpM3mBqmedsGU36kRNA0YZ/+9ffdQEarwVSR52y4J+XExhNMTEyigwhjDLrB1M3BA/T6AYNBhm3oIH8g9hL9JE6pcoM1Nfv1jzE1tYG03UHJgDdc/WL+x8N/RVFmjE+uML1hBi0Dzp6auwBTaZoSRSFJ4j/hm1auYMV1MbbEupoyW6U11ma80+bRYhJNQFnkTCy9luWpP/WZdyKktIYN02uYms6vZbCYUTZCqInJKY+pOKCV7mUgBdRz/PHKu/mIyBtK0yGV4r7ZcSYnx9gm/fhtKcrvCFNPvqGIJkUTRqVwGAaIdouiLH24XRjQbidYJygKR1X5wVhJHKGlxLkc52qk9EGGtTHYpjSzpuZrMx/iY6eEN8E1J0vn4H2P5hgB5/beSl3XrK722HziFoSoObPjC+Acm46/FaUkHz1uL6C/RvfNs8zk+8TTtG3KUa00nU6HDWqGKPLGQ1tbnFqLsn/nAyvM7+rhHKRJOnq4SiUY74zzqjIny2omJ8cIMk+tKKXYtHEjZdWnLDPqumZyapJ22saUNUXh06Lb7Ra753+UX96R88HHDUorfueylB/t+uv4yqXfZsPZd9JqpWitmev8FjqQ6EAye/SHmBv7LAt7b6XdTtmz9KPESTxy1Pf7jSxZKdJWB2tcozhxvOeRPrW5hcn+gLY7w23yl7zyAoHSmiTxPQec96P48t2TYlL4/9tnWIUCPkpIIrl5ueD+2ZAwDNj0og5SOeR5j6mJziG0uJqidPz36o1ICvbMXEZVVvS6PZ8QIfwwtb88dj8v3HIlde2wpubyDT9OO3sUVdXUUiF5ABxcM3c1AgjU/dS1YXF5N2EQoQPfnAdIokdQSmLdtc2o7TUE3Ttz6GkD1KXiVJ64hhvP8OtHrvjmdR9T/53r5t+HlIqVziobNsw0w6poUkCG95BjMMj40sHbwTm+b/+NIwe9lHBV+/0sZHNkRZ94coIgTLz1IXyIjbMbSR5PKMqM6+d7zO4/SesimBof77Dv8XMsNSZipe5Aysux1rKy3CVtpwTqQbTWjHXiEaaOTS9R18+nVT5MnGiCq9/Mg60EIQTXz/X5WnQHYtzPMXnRwPeyaTBVG0c61mZ28xQ/l/wlf/GNiKv3Xsly0mZm+Xpe0J9kQo/BxDGumevyrcm4CYkMeOFq9ZSYetINxToLzo5UXKbpdYShpjQ1SqvRtDBX+4mMRVHig1T8jPmhQsRzsX4+g2lekZLKDxuSdnTFh7Lg4YOyrmvPq0PD6a1NJvOObnHBZrJO+fes7iYO379zXs0VxzGxiZHNKdLUNaJa6x0VeUFRVF7q2URo1LbGVTVRlCJ0gBMaFcaIQjbqPcH4+BhhGDalrCOKvJrLlMYLIZQiCEPCKKSu81HQ49BRDaxNYpTe+CSkQ2tJEAR0Oh0WtTdZCSVIksgbrmp/6hn0M5JWTKhVozoSI7l6VRvCKGBsvMXsxlmiXoIUvoHpJ+T54WvWWbBN9pTwfZUnxv4/U5Zt+kDDTXToiA+DgNKYBlM+EFU4RnSLBISMkKo5JYlmY2owVVYlPvjRY0rKtc1ACuEHJVkPCo+pGpwPq1RSeWVdE6U03PDWRB7r4PQsxhSsw5RoMJWsYcp/Lk3vCNFQ8yVrEzIbTBlLFCZIFeJEiQ4jhBxOfRSMd74DTJkap1yj0JQXvM4giC6BqXG6q12kWcOUaDBl6ppBPydJY4JIoZRAKe8HNHWNqQ1BqGmPt5gNZwh19bcxJdcwZa37jjD1FD4U/wKl8CNjFxZXCbSilXhjYtQ00POiAgFFXtDv9QmUJi8UDosIBVIJpPLx9WVZ0BtkpEnK5b33sXPXH1GZNzAcrRlEcQMGGPotwHFg5cdRY34zGTvzdr+pRP44+vG9IR84VIze6TOjSP/O1/BCDSuVYZyGc44fWXodtaqpneX8nltJ7v9+/vM2H875wcOKk5s/ja41gY58iepqirKgLCpmZxLCMKZO/NnfR6D7OPudc+/kaPj/Nv0Y34Po9/ssnl/AmLKJYNE4Z/mp05rfva6D1hrV5LpJqdi39GN85qYNvP+xnDzP6K6ssHn1fbTH2qQzCWLu3YAftfrvrh3w8wuJd7lXFae2fJor+u8lDAKyfk6UhE10T82v7VT8zLzyAoIZGLMTCB3j8DlYNLlTVV0hHQSqmagnh4PI3AUgfSasuvbZW0II7p2doLu4iB5iSimiyEfP50WFkTVFsYapfxv5XseHpP8s902OsewM45tup7u6mSRJmdm4hUA9QGXs6NAl9M1eqdlM7ez1rsLYmk0bD/uxy8J7Ue6eTrlhMcA5uGPyMa4/v497Z7wk9x8LU0+c4vjtridSYhdUKmJ4Pvebxd1TjxKEAVec3unFDU2obVGU5LkF4Z9XB05sR2/URHHkU9adbWJtSmY2JIRRRFz7UM31mOpynv37H6Y9dhrMfqyzDBpMHT58BTd91zmU1tyw0KdOU3Sg0VpTlhVHxX+jCg3bpm5heuoYuGCEqXBmA612wobgcc6bRQgi6gq6C0tMTE4A8K0xTX6uYKzTbjC1QpSE1NZSFFdSlBW7NhxBItm0/AEe33wr2n2SsBpDyNevw5RBOrhpKUMrxd0b2tw1rbHO8bzzg0teh6eYKa9wjfdDSjEKlhtkGWmrTRA0qaVVSdVkCqVpSpqmlCcH5FlGL1zFWj9PvNVusbS8SF07kigijANWi4yimR9fW4vE75JKSZZXerQXX4sUgiIo/KlaKa/KGBoaBbz3YA1Kjm752vl4i2fntuLL7do5Vts/yK9v8Ua0H324wBh/0yb3vxql/InGNeGQSvsph1EUs7i4xKA/QGnF1OQkrVaLL8wLepniVdHAn0q1QjgYXPMl9N2NssgYzpw57aNzCkONYNvpd7DJbubwS3+PKEy45a7XUpYZx2b/kAcSS7qhzTXZT/DBIwWntv8R2SBny7feTntLCs7ycPu3SGbihp5UfOyExAQ1WZ7xHzZW/Kv+TyAi4Z3jMIr9rmvLLepWivxN1LYmKwo6nXHKKMJZgVCSKPaVT69fkIYBkfY9v5EJqdlYn0lLaY2ztlES+arb1h5TLykkv5Le2CQBV1RV32Mq8ZhaWV0hzzP6vRxXV2wfW2ZzYnlgeZHfrPtMpiH/IQr4RsshhPeXXH1uD2lSNH2SB1lY3M090496ujrwoZxSeZMdeUG/75uw9XjIPRsOjdRytsHU07XWbxjfiUdluIZ01ZbyFh6v55C1467JR3G5P7hdO7eXBzYepja+4nbS8c2zB/m+/TcQhRF/et83uHm6i9aKmekxOuNdinIHtXHc3nmI7Zu2k7ZSnr8Cl20u+eJtN3Di5AYeKTJaG3uEQUQW38u2nfcRJ7sJdMDy0jJRmNAfeMp5eWUX80vbSVttZq44jqmvIc9Lsn7GPj3Due2rLAK9E18jSWK6My/0M060wlQ1eZax79gmoqt9LFLdYGqIp9paHtx4BKkl1tZI6dD7no+IX0SuQsLgL2ils4RBxHL3Kh7bcpRWHBOFIcrBjUu5Vx/+XXsockQjuaZU97SVlN4d7Yf6NNTFoE9VmuZU5HwzPwjQOqQ2FoSPRhkMBgQqbCIiSrIso6qqJgm1SautDEXh1WRh4IdzjRruwzgNIUY5RLJx8A/50pFP41m2owypSW/2XiMqR3EqQiBEU7U54ceQWh/mtvb3/vPRzYClMApRShJohdKSPM8pqtzPq1GBryKb77NNYGBtavI8Q0lFGrVIkoSyqsgHGXZpkbIo6acDgiChKmsWF5dIW4mPbIgiSjFUXDlsbQmjkCDUaKWJ4pCiKD1N1gwJGkbMKCUpS38wqZvQy263i3UWLX2Z3gtDhskPWipCHRCI4ac3lAQyiiDPy+If5dp9u+tCTOHvZ+EHJIWRbhzW6zBVGRweUzrQBHWA1jXGOAI/p4zBYIDVNbX19FiW5QQ6GMmEh9ErtckviSn/P9FIisXIwe9f67o38GzFFIz6a+v/XEiBqNfk6B5TrpG9+9yN4UTLYIQpT/MHWq9hqsxRWlAUBkfdXFeBqz2dXxszwlQrTUniBNNgqiwrPwwvy7C1GGEqjHOkVERxNKoinX0CprQmjEPKwosytApGmHJ4TFVDTNU+4X3Q9QeVDtAeb1EJ9wRMaYSLuMDxJhjZBvKyJL3E5/2kG8rQ2GTdMF/JP7y1DgiDgKL0lUlVVfT7/Sb/Ro0iAaIoIg4TqrIc8fpZnhG0vNPyzPZPY0rL3M7PEUUR28+8HcEaFbLpxFs8rRYG/kWItc1t/U2/vufiHJ5n5ll373u3P8P3Aunyrbx/7u2EoeN3NvwZP7r8gwghWNhzKwAfPPImams5ufOzuL4d0YPedDq84Zr8MyURErIs48T2T5KmKZuOvRVTlyOO1DlvPDWlNw3qQNNu+Q2lVxb0+31kt4cxlqqs2X76rUgpmS/n2TA7zfSRH0RLzR17foWN+fsb6kCz+cRb+Z3LfRDhT56R5JmP0f/YKbBjbvTEUlLyK1stzkref9gw8ejrOLTp93DOMtHpMJ61MHVEv5GySyGIdEAQBnSzwUjEIPBS8tIY8rJ6+q7nRZZg7fpa6w8GUnpKOSTgY5mXiP9/lBdiqq584zyMiGPJL9ucN4sHMRiyPPPxVA7KsvD+FSHRWpPGBzH1NZRlRZ7vIwgEYeSFAM46blryKqbbJ+NR1eKb/WKEKRpM/UPgaf1s+Iut4d+tp76Ga31j/jupUoaf908awel1m+O18/t8Im9tqWXNDQsDLxyylmyQsW9bxT0bDhFHES/dcRVS3HsBpsLwQQK9QpYNGAz6gCPLDKauPNUofFS9mLmDsjQMFgbs19dyTfo8NsQb/CbS7/N/DvnQ0PSag7w09vTk/LnbmNjQY2xsnDgOOaE+zcalGZyDntL0Zl9I0EwzjeOQIiuQQnJw2ynartXgwIupsiz3Ig7upa5j0gcq/7r2wUz6aRbrd9EvS756XPGeXZJIB7TDQ9y4WPDQ5ugimCov+Vk/hWzYa/2V8IYsgZ/wFsWekzPGMBgMOD8/T54NiMKIKI5ZXspYWZKEUcT01mmWF5cbt3tFWVRMbp9kamqaNG2z9dTbRn2C85ffyuRjr8dax8yRN9OZHm8a/T5GP0n8TA0FfHxPyPsO5Q3l47dXOQy5e/ZtJaPlneDD3znfBXWWt597NUEqqRv6sTIVj01+wpsDbYhzNc7V2NpQlhlp6gMAq7LChCFV5blfVztWl7uYymC2fIoy89M3dRA1QZ+CvMhZWV2hPTZGe2wMpQSnT57h+erniPcmgKAem0Ec+Cuq0sDXX8TD6cfZd/7HSFst5DgsbPk8QRiw79C70LHioyebJGFR87uXJ0gp+fBx25yCHbWxZFnOjz+cIaXkEwdS8iznjW7od7K0x2KyMqJoyvfKVNS2Jgn9lLzSeE9FGkUoKQmDgGeaaBjWBCo+u875w1cSUfdrjPGVyS3z8/w70ycKI94R/QmfXXozbzl/lDCKmNr5PH5hYZlA30RlKlwe8N7tX2Zqapq7ll7Kq2fa2NLiigoXBAjuQQrDKX0vN06+iEMyJG8wdceUvxYhnvq5Y6jqaYQw6mnC1MU2nb+rdLgyF9Ke153fSxBosrrg4LbjWGu59twqWXEFvaVuY7gNqUyfGxaOEIUVC+YY0QhTBhOaEaYml+5gdayDqQxZ7iuMO3ddydl4mpcv/ypw4whT2XhOe/wlKCX55Nfm2HvZZfz4KwJAcObMZjZsPY4pDXNnBHfO/wrXFh8ibafIAHB+7tG1nfdzMDjhBU1NIKgf6R00YyQENywOqI2lN7iMPM+aENWHuOLkDuwGb4aNsr8mbt/IbZXH1Eu2ViNMPbRpgtWBhAZTtw2u4CsaKlHzEe6+5Gf95CqvpkeBwA/XshaN76348roJphNeJeEf/MZnOhV+tGnP+xtH8ShaB0RxQhwnPqZ++2d8I1VJPu/exg9s+wO0Vmw79TbO7fpjPiduAYQ3LeLnRFhreV35GYx5k99EhuX5evmBeHaKvPzj1fHxPRoOwq/tkGgFZSUYH/OxNe9+6A0cnf39kUHOOS+IiMKIMIxwzWlCWIFQgrLwAY9hGKGcpt1uEycxG4+9hfndnydJUpIoRgrJubkzFFlOURbESchD6W8wPt5hNp3lv22d4nXlJ8kGAwITMP7wD/gNfpuiH40xv/MLKC0plyq6vR5xFNExNR/fE/CxkwCOfFDgXDiiBKSS/MYuT/W8/5BgbtcfUxvD6wpLlg8YGC/4CHSAqy3GlSCGqsA1J3MahRSVoXYW8EOBEF7F9ExaPpXb35VVYxRG+57hbROSfUveET2MHi+KgoHpUUg/BM1aR7/bbZJmh5gKiWJNHCd8714/AVU3vUbbHMi01lzXeT5/0wnAeQy/cNVwV7I29Ouac3u5b/YwQkofWfNEl/w/AJi+k0b7P9QSgpGMHnw6h6eKS25c6PvP2ToG/f4IU1hHkReY1BCGEaa+FmsfXYepcoSp/fI6TjWY0qZCSs1L5o5z5uwZTsxdz9atF2Jq7uxZj6mNG9n0okmK0zvIsoww8ukISim2btuCPnczyj1AlUnKYg1TtawbxgjAkQ0aWlfSsAKSO6dSnLXcKA8hXJfa1HyzHZKs/B6bijf774xy/n/y3jza0uss7/zt/U1nunONKpWqNMuarQnLA4NJAiGesGxsmWCMjQ0BOous/ifp1d2re/VKr0VIFt29SAzYgTRgwBNg07RJYgy2sbEsyVJJsqaqUlWpSlV1x3PvGb9hD/3Hu893761JMsSW7N5apVLVvTr3nO/7nr3f4Xmfx1uPQTD11ydjbrm9ycx0k7Z3vGFo+eqUOOj+4NxRbl8diWT/JTD1InMo4tjlnaOqSmxVYSONMZW8cDj5pTmvw/BPIae4ETMfU00GziYy6xFpmhInMRMKY6SluTwux1hn6/puWVYYbYR+jK9/Xnhzdb9gcphsK6XXRdKX/uC9nGuSlShFrVeGD0NmQahNMrlNgCgdQbBmnpRQJtd44rE+6Uk4F9WllTioO4t3dkNojanoaW2sr4sLoDN1uSOOY2bn5kizjNjKvYmlcSHzI80WSZoB4gSYpmldr45qqrerSw8qWAxvvTdbPSlcoIs7H+iUoXxjKotVrk7nN5WnpfZrtMNZoVRPnofolcYbhm2YMqYSU7SgpGysZAZJkqILwdq4HFFEOcZUKKXqATvn5CCIdESaxgFTbMNUXhSIV1FcY2oiseIRu+5tLZItPcptPZSAqe+q3mT4YBqwaiumXMDUxGI7YMqLxppDmtgA3k4wNbGH2IopUWSOJpjSEUoZGpm41KYXwZSQaGJm5mY3XRm1glh6WhNMpWspoHDnYir0Q4XpSCjzhz9P7k34OdsxJcrBE+vgGlP64piKtK4xNfmeS2Hq0uKQkaIobHiAHXmRh4tiKY0Tva04Je5EjEYDinzEaDgkihSmmpamq1KMi1zklINvSaOREcURRVmCh0/5d+IqxxuW/j0LG+8jTVNGasxoPOYnn1tHa80wSVg6+MekScKfN36KXzj+E6gkMKKcE6622u4w/12nQbSl8a6QDUMA4HnvkwMWFlKZZo5jWq2WzPSYijzPybIMib7Kevq6Kkv6/T5xElMUGd5DkqYYa9BG/GhabWkQpon0tTY2+mFGIsY5z43Dn2fv9GW01ppc37M495MYY/g380N+qSEbvfWW3c/fx/JVn0Zpxc7du0LjMKEz1eKXz0a4SAKKrJHyi897lDIQ5ll+9nBOVVX0RiPK2RKxNZb3uu+FdwUbg4Rnp34X51Q9RzDJTnUIfOIQeIzynGYjQymN2tZRfvlXHGnBlKnoP1ow6PVJkpid91puPHsVLnIkcULU1ryr+CiDvMdgOOQfRR9h3G6iWm0idSvPVL8DVmGsZbG/n7dmV9OJOhRlgWg6eYyp+PrKl/jSjp8jJeEd40/TWC9Yn7sLrTX/W349bxgeJk1TskZGo5Fxz8arArHmkLzhgKlvLLQEW+rlKyhP+irfUpYzwZT3JNHjnFCf53r/PpzTFEWJMY4oCv2mVhtjRNH6V8yIt+k/YY7bqaqSVuNZUaioSvrhnkmPBHFotIYqYKo99Xqazc9dEFMH7KvYvecy9u7dy/WtJlG/wk8/R7tlWF7ZJ0Z2AVNREmP9rXJoXb+b0jlIE45OnabdbAWqsmBKvIkI+4bj1asjTFWxNriaY+5jOOeZW3O4NGV+6mTA1DyrSz2uGz9Abg1HtmDq7m6OV3Dn2nUYaynKkn87JaoU/8I8ftHLfckDZTAQGYg0Tmg2mqwsLlPkYK2h0WqLF4XWNBsNms0mVVlQlYUYBpUlWkdUZYL3Ep06a+SwMRWmMswdeSvTUx3esucPqKqK/3zZf8etuZS2nIcrztyPy+R4iGPN7hP3Ya3jXW6Vaqqz7TSFzUhKMr/vssME2JqFaaWwpuLnjxQkSUzRbHLqsj+k02lzxZn3sPfkO3nh8k+gnEgiLC29QLvTYnp6mumpGdI0I480o/GINMkALS6aPmdtzZCkCc3eBh0cUSyAgkAVDz2py/Zdzny6QLvT4jevSlheWhYxz9hSrlfE8U7iOKLf65Pf/l+IBpEMWMYjsTpoyiYFoamrozrC9U4ipn+7I+eXTgmD5NiO3yd1KVppdBQx8/SbyOZEKVcrRaOZkJiIIjdMQjM5LsK0iVYiqudhfaMPeGKtgIXv4D289BoMh9y5OkJrzZOvmWfls0sUhaI5CAejEx+ZZvN8TFVlSRFFHG3+Lt47DrqbMMbQnF/kgemYTifh1auG6ak2w6GwJ1+35428Xkvv4aGZO0WCIxBs/mX+DA8kKVVlKIqSB2efDnLt8NjOJneujXl4rvltw9S5DfpLNewv1KR/acvXw6Pe3Yoxn+Nk649IkoRd9u30B1fT6XSYmTqKNU9zeDGjqiruL0vOnL6aR/Z/henpaW7rvJY0TclzzWgkmKqCoOk4z1ld65KmCb2RY2rGc+VVT5AkL3DkeLwNU/v27Wd+YZ5WR9Qszp45S55fHwZYe8xMxzWm2tMtRoMRZV5QlGParTbNVka2BVNab+qsTTDVW+9RFtL76G0M2Ju9A601J6NPsL+8nqyRBsUNwZTBUeaWH9hvuGptxIyJIG2gEWM97wOm1gVTG+7KvxvLC0QgMo5idKTChRHTJpxMjaI1qpHVKaAoaerafCbPY4k2Q5SJRwa78DSbDfLxmJln/gneee67/ZPA21BKsXbNZ7js1E/UpaAoyKcKMyaUhraVdzfNt16pU9Lnru3B80T1Uz7fBw6P+R/ab4ZSrAKiyIXZG8t/OKAYDhPeVMgB0Gg0qMqK8WgcmtFpYMM5cceMAt3YeeIkqqOQ7rV/xpS6Img3Se9LaQVO7vW13Z/h7N2fpJtlvLFr+Zh5Cx86WqKU4qPXdshcgnOes/s/xbWnf4YiLynykpXpT7Nw9G00Ghm26Woq5iRl15EOfaKEnzs0gCRCzLWEuuywLBx9K4sHP82o0yLSQi4YDwsqXeIRAci8KGiUKY00RbOZuaRJSm+jj8fTbCQv09298FLAY7umxIlUKzEUm2DKi9qEKGs3zseUFSnzPB8TdV+D3pGfgylqTOXF9XjvOR79P6zN3YVSirvXcrIsrZ87HcfcsyEKz1+flhL1oeAPcufaGIWqyyhfXL+aH5p77u9V7jr3wDj3MNn6+4u9zsXWQ3Obr+k5p+wTnvMruYlMZYyiCOeMZBdBuPaK2V5tdsb6GunwKjqqTR6P8V5KRs1GkxPxk+y11+K952TyNPes30qSOqJGgyR+gn3lO3HlSZT67DZMzc3PU1UVG+vr/NVxxXgMr7tMestpmqHjg1jnGeernLz+ZI2pyQzQxFAw3oIpBdy9UeCc44GpYJ6GDNScTj9Od+4eUHDn2jUYa8nznKoUr6PxqKCyYsL34EzGmbZmtqOZ6WRoFM4dwTnHjWevJF/fABwqfRa48YLX/9K0YUQXP0li0Cqkdi6kehZrDD7SmxxnJ3W2JEnwXkplZZGjo5g0TWvVUu9lzD9LE5a6aygV1fVfIKSHWmiS4VTQSuqAcShvbROCDK93bsby3bF8mDPxNe3Vh+tZOXFgmzSHPF42VsZMpqAjHaEiaVAXudTLkzQN4pGJbOq2EhYY4oIZBQXZeLIZOYd1UrvXSuF0RBT0w5bygvFwhMOiMmkAx3HEzMw0SS7KuQolltBJQiPL6CUZOphkmSAnIjX/TVaX8OnFZ8WHXpGOqCVZylIIHvl4LIN/wzHWeopGgQ/y/XlRUJZCa/RbZinSJKYKcuNJ/Mp7ZRoyPQAAIABJREFUIOJYZhjQkCYp1tlA23a1748L8vRbMVWUJjAlc9IwbT8xyKpp1GnMUreL9RVxvHmYTjA16Z1N/m6ixjCxg9i61BY3x4mNxHfDmgRlPpSL5VAR1Y/KnIMpL+6X4/G4nsnajqm8xpSIRyYyF+JUqLwETCnRQ9vE1KZq9LmYKgOm1jegkbaIk4Q4imm2miLBcw6msiyrRXe991hj5EDx4J24k9burM4RJ1G4r4Ip712wmS7wcB6miBIImCqKUmZa2udgKg62C95htLnotb/kgZLnY6w1ZFlKZ1rUMa2Vm1GEr+lIk+cxcRSRpg1cS8QJ35GI33LNaDElysrg21UrP01z2KA7WuPw3H/iqpX30mq32HH63bxw7ceFxpimmzM1arNmnqYpWXppk5fvtjVh2Rhj+fX9AoQPPFugN/6A/+Oq99PINP/0iT7DwRDnLG957nn27N6Nde+gLCuO7fxd5ufn2eitMxgMieKUJB7TbDaYnpmi291gNJI5kyzLGI+GoKDpPaORTM43mk1A+lBJJFP37U4bD1SVYefxt9J+TYskHeOc5ZfP6nqju7X8Z8EUC9Is4Yb+B9AL0kysS3g6lLzC0JBzjp95pqCsSp7f/fvkeYEZGqampqiM5eSeT6ALEbjTaLSOKatKgo9YgBahUE58JyYlL+ehGFfEWjMuK9ZWei/fjb3AKvIxzhrSNOUHbEzvtTPC9FKQr4zrhnpenI8pc+ZWmbKfeggAY0qMlbmS6ZkZkjThG8tfJFt+I/Pzj9JqTrOncSf5eo7WOhAvwhtR0heZGJa1EDOyO0L0ryY2EOHbz3VF/LusF+t9PDTfvKj0yrlZybnfe+66fXkgciNlSVEUTCblx6MxTzS/TsNl7OjvRSmptJzpj7j7ehFYLMuKXm+Dufl5egFTcZwy2oapdZbMg3gEU9VQZk9iexO9wSxxvEyz1eR1l9/ASXVoC6Za9Lo9IdnYiN2vmyVZlEyp1W5IiyFN2Lf/Mpb1kRpT7akWOjofU+iIu7o5PgSGty31KcuCjcGAIi+Yr95Ed36JylhGo5IT8Te5KbpLxkF0LB5LscgbJYlgSXnOw1RVGOIoIh9XrA37XH6R635pLS9jSdM0MLjEv3pCOXTWbNLwioIsy+qNsapK/rwrchr/sL1Ry7TEUYwxBc/O/RZJnDKOcvbs2k25/wusac2qB1Vp4lgx9+xbWLnhMywcfWt9sYQqt0m7nKzvvqxk+5owSKIo4l+cFcaFbbeYclOkLXEtnJ5WVNMzFMUYvOJQ4z8w9+ybcc7Sb/QYD8diWoY0YxVi4Twei01ynovk/KIfoIGWb1EWGcPhgF5/g+GoX5cG4jgmyzKO7fi/2fXc27HG8sKBj/Nj42lO7bU4a2muv5/fvWmaKBLK6weeyaXWHmnSVHj1KM/K1X/KZ5P7Q6mM8Peh+RdFLB38YxIjU7mD4Yhdx96Bc56qsqRZTLNokiQJClhb73FkLmNsTJDm6TEY9FleXiHZQmV0xjMa58Lisa+sprzZiqm8Yu1hMc36/gO3kNshJ9QfoJViNPV6bs3fyzgZ81+OP8T+yx+lnP4bYVj2DNNViUJKZoftIxw82qWKU2b6b+PMLX1W4l1orbljrc+p/ixxHLN+vSJJEu5cG4GHm85eySPzh9mKqYfDJv7twNSFeiRbD4VzD4hLHRjnfs3YWwC4c+1xPB6nNbFSkKa1S6biUaampri5dQ9plmLiExh3C0Wes9cf5Munf58D5gbJkp0Ilz5jHsGoirad4jpuZxQwVVJQGkNlLPPlXgb0afkWUVIyHAz45vi3WCqWeF6fqJmSWZbR39gISiMpzhiWv7ZBvkcCia82DpFljaA8HGErGdsQN9VEZoGCx7ndMtLx8Lz0uyaY+vpMhm1HVFXFNccj7ujeIKZdM49yZ/YGms0mcZKggbWVHqOiIjeGKIrQx/+KUaQ4m8ScPn0Hr9/3KvBw3B5jtRxhjKNbHLzofbm09MokDfZhDsWKD0pllNDtnMN52ajiwKWP4whTBfMnFJWRGqUxFR6HMSXr3TWSOMW5c5WCfUi9VV0rdBP63pZ/f6+tWjJFqeBVQR1RxklCHMckSSIPoXfEcRK0nqq6/p5lWc0Km6icirumwnmLs6IWPRoM6/vknGUwGDIY9BmNRqHmLFlBlgkrLM0ydKbotDu1hINHPFqsbdcWzZ4Jy06e8s0NabOGLXMjUjKdNNXl9aRH0MgapOHnJrGj0cqEYh5Lo7IsZDMeV9LvKfKSSnmwDpvEQvG0jqqScpB1jrK8eHr+cix53iW7FkzJAV3k41C+EEyJgKR8fxz09Caqt5uYMrhzMNWpxiEbVLWyhFhPSEltousEwR4hyItM1lbG/XfzEoVlRYS4DkqvIdnyPCUoNNpL/7cyhnJUUdlNam+WZaQ+3YapyXxXRSXX0hiGgxFRJLI1TWcZDi+NqawpPb9O2wYFYsk48jwnThJkdHsTU6FDvKXkGBQhAt5cra3lN3Hvg/NiIxNMOdC+SaMlIwJxHJMETBlG5JU8b0VRBExlFHnJeFSIinFlsYnsyWVxcX28Sx4oWUMmjp0TlU0XGjpFUdDptLFWLH0jF+Er8YnXSpFmGW/eUVAUMB7FWGsYjQehRlewtLRIs9FmZnqO9fU1rlj8KRrNJo1s0wL1N65JeJupOHPgUyjkZi0ceev2h/+7PDOZROzbmGpssSDVig89V5KmYIBdx95OZSrKVkmr2aK47a/w3tGsmszNzrO2tsbGepckaWBdJW6GrRYrK8t4HxEpTVVaCj8W6Q08G90u3W6Xfr8nTUfnhQffatNutZmemiZNU3aWv8jX+TWxA04T8jznA4dzmk1FEnsqLcZRUTzpk8lmNn/4LXwwKVBhU/zwwZifPyaZ7NkDn8INDUUujf6rV3+aqakOxjrOXvEpdizev00mf2qqRaOZMhiXgCIa/SaNNGX3zC72nnwHv35ZRF5Z/sHwd5iea1P6ku54HfhH37mb+iKrMcGUdeR5zhv2FeR5zvpGl06nw2XVOwB42p2iqkq8sxw8+DgoYfZ47zl18lVM7TQ8kT9wHqb2tSzr613anSkajSbf3DfPa/olULJKQ5xTjZH+aPw4isb2N7hl33oxmZSt66VQei/0tUtJrVxsGXsLcXRh6qq0ejY/RKQfr0kI1qltfSGQvlKayqHR8i2W1Um8d+wvr2Nudp7mWouN9S5pLJhKophmq0W1KuVXrbT4nPhSZkXwdC+BqVarTafTIU1T3vM6zbGjxzim/4gkTVj+2i203tiuy/tKO6Io3hzOnfRanVCdJ/qILihLS9m8Yrb3QI2p2dnbmJ05Jtp2VYN2pyWHYrhenakWeeUYjirmu19jWBkaaUqv+4P8+C2K4WCGYlxSlANaM03KouTkC49d9N5c8kDx3lNWFdYaNja6RIEF1Gg28Gx6LY9GQ+JYBhitk1Nbpn0j5hd2cvr085RVjneGJE5JkyazswtcccUVOOXxS56iEJXTxYN/jnOO+x99E6ObQ8YSRXwmup8PEGSTv8sPkoutOpCfsH7CQCNInyVNA1MOxcLRt9K74c9J0waXvfBuBgc+h7ElSnump6dwRkQDu90ucRLRajXQOmZfpjl79gyD3oBj4+MMhn3GY+mHRToWBdJo6xBkhPOWYwu/C6sCjDRJGN38X5k/cz/WWnSYJ7HKgpX03oVBqO51f8aeE/cB0iB9/zMlJAnOWqqiZOdzb2fpwCfFyC0SHr+pDNNP/RhlpwhqDQpItnmJaKUYDYaM/TrYEV+5ZwettT5FPmbP82/lhvZ1rHf7fHn8qy/LvbzY8t5z61IPYw2ffrwvtExiXneZbOwn9B9ijEE9V/FE/IULYurVd5zh9OkHKJfOx5S64gG+v7qR59YfRCvNaM/r+UpL9KrMYECz2eSR+RZ6QnoJlNoLBWeXKk+d+7W/ywT8S5Gqv9hBs/VQmZS7tv735GuVvSWkXMKQvMJOkRdNjBVJmlbrCeLIE0eG/b13kaSJ9Czmj5BlKcaWaO2Z2oKp9YCpNG0QRTG74h9h8cxp1ruO4fA4T+YfuQSmROnbeUtvfYPK3cSV0e1kOuO6a2O+ceg4RSqs2ObNGqcsNmDKBkxF0YRA4Wsfl4fmGpSF4pq1A1zNCBtXKK3Jkm9ibUZVGb7WiWl2NM5KVeTeQSVulPYmrB+QbkQ8/ORT4Cvm544zd8+teJdQ5mPi2PN/7euy3u3z59lXL3pPX0R6xdYRZz4eb9lkEsbjEdYYYTMYQ1WWYjKDIs/HyOxIQpaJlPJkojPSETsWdjAzOwtKkY9zTu39I5TStFtNLj/8HhEybCimjr+9LgX9rM5r1sH37ImyZYVgJNQgPB8+oGm1pEb6s4cda9d+lggpD3baLTon7mN9+qN4D7uffydJnFKWJb3eBk/PfISqMijtGNqhlBSVRMn93gDnbT0rhBKGSlVVLDz3VlZv+wwAsw+/mXjQZ3jTf6bZarLzubdz9uAnQ403IlIR+154FyreNN9SSrHr+NtFJTV8KqVkGM86x8Jzb+OFg3/IzmP3EccJH71Z+iVFofgn+ce5cuUng1qD1IzH45KykklmENZYPhoxHvf5p9E6a8sb9Nb7zMx3ao2jcZ5/x+/dpZazNoxuC+ll4jUEslkV5H9vTD01/gYulBZnnnL0b/LSeA0unToMJ96xKoKak8HFb3VdaLO/0MHwd/U4ge2HxcVWHD3+kr7P+0nQJoU9U0kpPtIRqNvQepHnoz+kmTS4o/16KnsLxi9T2R6Hdnd5Tc+SZAmWW7Drh6T06BxF/jVUdACttDynz98s1Rrv2du5gpPxx2tMUY81eIrqRqJoQKv5LK12C6001SJURuRXoqJJ1pCe8lZMbWXqeTx3dXO+sSCVJBSBOpwRxwmNZoskTWSIM5fMfhI9GOsYDK+nNCOcd1wx0+MrAVP5uM/fNG9lf3/IcDhiZr6DjgzWWHYO3nrxe3HpGxAGPhD9m83anWY0HIVNyNd0R2nQRhRFKbRIHKgtTXQvTcSp6Sla7Ram5n+D1r4WcYsjYR1sRk0qTMK/6DPzPbG21rBFJkHjQs18Uh+PgkS1UgjLpJoMhErNvNlq0GhmQuEuoChLvPcMGJAkEVoleAfOeqy3Uq8N9ro+zBipcLhY4zBFHpzmklo6pwo+OApFljYki1JpGOLa7AsRmF2Tv5uQN1BgjUPpiChO0MGz2nmwgWM/CR6E7FFh7eT15fksipLxuM94YcRgMGQ0ytl3cC/GSHk2zy9uBvRyrIlyK3hxA8TjvfS/RqMhuc6/DZgSEcE4TtBbMOX//4qp0MdyQeBWJ5u6clsxZXPpHzsnmGq0ErJmhop24FehdCUOz3A4EKdD9CamnAv05UBhPg9TljwvtmBKKg9VNaodarOReEMpJb1TtQVTtWvtFkzZcOhY64jiJGBKEgLnfI0ptRVTZSWZD8GPKGBqNO4zHo03MXXlHqxZJc8LxpdwhLjkgZJMJAVCqo0HU1nKcsBoHGjB+MACM5ReeM7yRoUVVuQFRZ6TFznOWaanpllaXmQwHHDl8ns5uGcvH7lGSmgfPFKwes1n0Fqz+/h9JEkcUkVp3E8ojt+rq9538cGESWZ/kiThF08OieMSqDh79Z/SKBoUeYF1PrC7tMwMxQkeas2u3qs+x8E/eS85/4nhaEi0K2Fhfo5Go0maZkx15jh67Fm63dWaQ58kCZ12G4/H/u09rHfXOZT9L/xQ+3/mk/Y+VF/xi0VOkZfkeUHnqX/M2as+y458RLPVROuIDx+MUCh+/rh4vf/W1TJs+cHDBaWpqCqLMY4dJ34Clcph8ksnJRMuq4rfuPJ+fqEsiSMtOkWR+EikaYoaV4BEbVEcMT0zg/fBxyOOSJKUtbUuq2srjMzGy3lLz1tJmuJsUWNKeaFlPzn+KLuLA4wY/b0wNTU9Q3rDm0DJ8Og4DHta61AYdCLDjFprdJrwtemUiUbTS12TLOPvlm1sVwx+sde4kMLwRMb+QlnJozuPcPvyNfXr3r4MXiF2tsHOfIIpU92JcxF5qcCXNBpNDhTvIS0TyuoMSim+dvYwvY0e+2/aVWNKR9Pc2PgQa90vCKaymB0LZ2g2mqjoTh7rnaGyn6LbXeX56Gvg1TZMfc6s0+12efpzX+LWW2/jVdU8ak1xYOEaiuIh8rxg156vc3bxHvZGmmarGSwMLMr72mBwQh5w/jZuPitCvaUtuX39RhmvyFKOHjgT5OvFQrgsyzCPpHl0Z5ubTIui8igqvnhSzN9qTKFrTD2weJQvzl3B6XXPj+bPXvR+XfJA2XHyXp7f+RWcl3pdmibif1KWRFrhrJx8lSmJolhOZWtlop4JmweKogynrhhyOespi5Ij8/+J1c48P7SWEccxw8HbmXryH4v/SepZveYz/Km+H60VP3e03Kyhf4+WvCZnpUKacv2pt/Ib8yL8957HhIIbRzGXnXgXv31dk/c8toYxlmPX/R4HFn+KXcffTrPf58xNH2d6/WdRSvEpdx/v3r/G/7j+iwz0kNNzfyRslGbGNd3385vXpVw5/FdURcmoGKKjmPF4zOLiIg9c/qtkcw3iXSmvPfUvmZmZ45dOCS35165Q/PyJu1jf6HO2+widZ65juHPEDj+P1poPHikAJYOv3vOBZ0ZM7l4UiTf26jWfZfbwW4Iel2bx4KfpfPNHUQruU5/g6EKfqpLmYpalZFmTqvopwDF5DiIdk6Q6aMQ1ZU4liqkKSzmuKP0ryw+lN/QUlcZ5jUJE/8qq5PLyWpw2aPf3w9R6t8udTx9HxXcSRzGP7TpWb6DSyFUiWa8Ud3Vz7lgVJpJGBX2q7e/3Qk3zl9Ksv1CT/tzD4duhPDw5bCY/v4KgrKFqgpG1VphfUjUMFRHR8hoOBxhr6HYP0Jly/MCBm+n3+jxVPk/hX43zMc5Z9u3fR3vqzQyGQ2z1IEkc0WhmzM2f5n1zb+L//ZLBlyfpRZ/fhqkj8WFOLbaJk5Qbrn8VMzOzHNx5PaaqGIzHDPOCjY0+33x6gT2vW2F6dpoFPy/WEj5w8sIQ9IRx6ZyXnliScGjnUazz7Nz4BlEUYey9OCcDlkop9LHPUwZMDfe8loOjXWKBgON1l93A6SqjyIc8sPQMWm1i6vNXXYc526XIDW7l4jNJL+Ipr6gMWENNm6vd9WKRjFBBjjuOYwhNozRNgxSEqmmLcSR2o2mWCQc6lDdE/VSktuM4DgwMVael2x9wf87v3xvrvKRrUiba8jm11iRxEiigkp5nWQOlCspgG6C1bNQmULwnE9ftdhMT5oZckPaI4ohOp83MbJNWs0kcJ/h8IlEhoGs0WrTbbRrNJjuLXSilMbYkLw1lFeF0hHWK4aggzWQ6vapMaNJ7FB6nCD9XXjuJY4mwrMWEifhJiWxjfR3X7+OcZWNjg15vAxMkYZIkpTPlKFTFRFIjzTLEyVCTFznGW7wSE7GiLHBA2jiHxfQyL2sVpQERJhB8EDKEKI5QRv83wVSERet4E1N6k6I/oaLKmmQn3+OYCnT2reMHWulaKV1txVRZ1EPdOrAaTVXilMGGQ6nd2cTURmFxXqj47U6bZnOGVrMlRCW/HVPNRotOZ4pGs0mnM4VWkZRny4rSOryKsU4zHBaUlTDyzBZMTUgpWzGlw3S/Rfx0qqpiNJZSb299nbIyOCuT+1G3W2NqkG0wKIaURm3BVIpSnqzRJC+KTUzlYtXu8aTZxQOBS3vKxwmXnb2XqhhxtPPZug4fR/KQeusggljFciOqCg80m6LaOZHRaDaaJEGSfm52jkazFeYXZKPY8/xP0Gg2mZuboTKiSBzFMbuP/Tg/rzYbWRNJ9u+VdemP4mn3/oQPdf8pWaYoooj9p++XGrmteO+TPbK0gULKkL++Hz54WABRVSUfPhiRJBEfeGZMnCaBi+9Y764xtzBHpCOazTa/dNLxZ4HS65xDh5S61erwA83/iVjHqBL8nOdfTw344InjuKjkn680ibMOadOG6fWExYN/TLx3D/vPvIsPH5Ta7T87LvpJv3FlglKKXz4jUbK1jt/t/xhvWDBi6mMtnPlh3LSIGlan3yh9gySRcqvSTKs5oqRA6QjnHMnCLxNZ6f8cynTor8CpliHKPsZoPqc9/lffiVv5klcUJxSlpyosP3TAYe0wBGoRzt9GpJ+oMXVN/D5KV3I0/r0XxdSNjbvloFaKr5xp8iPXPUOj2WR2bg8mYEok1jddTvWWWvzD801e0zcXzRq+1WziYppdW9e3Qkt+qWsCqboctvXsZFOiXa5HRLPZrBl01hrBlIJBv09RFGgt8yNTS1/F7LhH+pnOkaYJUSRzVY+sf5k7518vuGm2qaoUv+Pr2PXDuI1NTE2X/5Abb76FatdUCJQ9S4ur9I4PGeUlSaNF0uiQNowEA0mCtY58XHD3es7XppItDXXDxFtHR5uYGg6GbPTXOW32Yq1j/tCnubx6F1VVcdT9HkVR1Jh6/vEl2rd+lCjJUFHEsyevpcxHOGdpTc1y7K8X2b1nFu/hNUsf4xPFfagkYvqauy56/S95oLSamrKEIjTnh8NB4K8LfTUK0eakea+VIlZCiRNJj5gd8ztYXnXEJkJH0mTSWpNmDdrtNlGkaa23SJKEj1yT8gvHN+VaztUW2tqk/d5aQTjROxGB29J4s87WkdBTMx9hMByS+zHXdd+PmpZrsvvkO3jvqQ1OHvg0VWWZf/ptsCdgSYml8m8cjOjOpNx22LH31DvZUewk3zXi3+9XXHkqNPQd4D2NRoPZ2RlG+ZA0PHxRFPPLy89j2x0aMwnoGGcscX8IwL+Z28evnriL3eVunlr4KD8yTEnTFOd+gnxc8P5nDEkco9rNWqftLdksx8+cwHkbpuxjmXkqQ1k1jvCBKJAksWQbSozbnPfh2ogtwjgf1Z4fo9URnneik4ip5ovqn35HV6upeWRHk/HIMXgoZmOjD8DcXS3u2vDnYEokhq6qfpJF/6cviqksa9Bqt3nzDil/JEnCXWtjDu2a2oapCYIenm8Gz3p5Vh7+NpSgXpY16UMC3m4OWYv4qVDwjRFMPXbsIcFUPmZhbp7i9ibew1UbuyiKnOP+Y1SV+A0d2nGUNE25ZfFKnnO/R18N6UYbPPfczSQ3TnNmR4s9+YjRuI8xF8BUa5pxPiQvIiIdcc9Gxeecp9GeqjH1w1MzrCwtc/ipZ+seYZxolk4uMtQzpFmKVopiFBr6SSyCnkoxGIx49TPX87Gv/EWNqVV9M8+4x6W/Ut4gU/haE0UxSRZzdfI+Tsaf2oapypQ0b9Gsf2PEeHaMVprW0o/QjXroKGJqbuqil/7SWl5lRWUktTq48n08ov+8jmombnHeCwMsz3MZagzRcBQUbaM4kgEtL0M6VVURRTGmKslzzRVn34OPHdYa3vvNPqYhNr+TafmtnvGTjfe7U5r+YktiKplxVDW4J6tmdWhFvz9gdXWFXr/H1HWfYD14fpidksJ6S13a+IUTniwDl2ZUZcnPHbP0e57HZ2ZZ6fwJ1cwM5Y5dfOjoO/lyo0mz0UKxEjLQiH2n300yn6CUDim1ZI2oCB/FeHSQgZBCtEIkeZI4RgOmLIlCyTIJtFi858MHIz70XEmSxNy4WNKbm6UMz8fBY33JpFzE0sHPMRwOa4MtrRSul2Bav4DWsTBnrMMUFa7/6/Jzmk2yVKaRR+MgBZPEvJIGG/Oy4rblIUU+wl52PY9PfRitFGcO3cPXGs9y8KCIXe4vr2OU52ilaTaeviCmou49JHHC5fOZGNFVJV9ttXlD4cNUvOGrbU0j9GC0DkBSqh6Jnwgpvlx0r0nPI44ef9FsZaunPFzEEtjDbctXy9fC57t9+dr6453uP8/MzC0opTiVfIJutMxqtcLo9E1MdTq0HpfnzTc9J/UfbWIq9P2SNOWpfSfpLIp0fBqn3Hrr8+R+P2We0+PVJLGicQFMXbunkrK1kizx0R1topWKJ/Ycx0cJt65cV2PKe89C92HuWPsRLm/soccippLetcrSWkxXFEUmZeGYE9cucfvgecqqqEulk+qEtZannryGA1c+JpjSisPlYSKfoXXCD+63fO4phylKmicfoLULTvhDZEnG5f7d3Lf4dAD6xRW8L3mgDEcVVfAUVtbivEWHKHCiGyMfSGxGG5nQVFG6rktaa8SSVGuI4xAZSBmkLPIwxOewQOGLWj1Vq/DWtk6RvwKrXVvf038LTBojjdaJe99EfdYakXTYWN9gY2Nd+gT5WAYCdYQKNGLxRDBBjsHXWY4PrnPtdgfnRDBvNB6KlEuSSD9i8jm0IkJvyqcrqd967UX2WWtCABT2pc1a7qSUUBQlNjYoJOq2Vu67saaeb5pKWixMpfSHA5xzzM9KoKKVQu9aYGlJTiqHx1aGjUEfojFpsyN0Sa9wxjAeDIliRauRkiUxUZQw6o8wtsKaS3AcX4Y1GAnDzU4Uuu0mpqLQR/Teo42oR2dZVps5nYuphrUQJ9gtmCqKHHxjE1NFUat8KxUzSfK3VIFeeWvre/pvhClPcGoMMlBaa6w3NaaGa91tmOowxzjJa0wZa7dhSmSNRDKo3e4wntg12xFTnfjCmNKbVGWlVJCf34IpP8HU5ofWWtVSSdZYbCT7Qxww5Z30ImWeKSJJWmRzs/SHA6yzTE+1AqZEIf7UiSadVnMbprKWJ21OxEAVztjzMNWJm2A9xlYUqrzotb7kgXJmeUisPcpXHGl9nnIokthZmsrgjp5oRskgFpk4MQ5HQikuipLTZ4aMRiParRZR0I9J4iRof8WsXPMZdh67DzwsHfhj0iwhSzMuf+FdW3bozQZiPRrzPbM2P5s1lsXFMxRlgbWWNbXK1/eO0brAJo7R0hrGOtKsyYnjJzC2qh/sKJ6QGBRJepiz0eV0sg77XngXeT4mzyuKoiRvjdExlFVJr99jZXWJQdqnMkXHF4I/AAAgAElEQVQotWnKouSb+xRLyarQT53I5t/aO0NndiftmWbNOFGRlCX/+9XnsTv3Yq1h/9n3cHj2dzCVwRqxST11+ccx1vChIz/N0lV/go4idp+4j+tOFzw8Kzvcn7z2Mu5/ZEU49d6SNaRmXBYFvX6f7uoaWTwkabRAwULyh2i7zJmVFXQCNw1+hisPHqTVmuYL0b9mfdCndH9XY6ZvzzqzPOTAsED5inI4ZM/47TRbLf7yumnuOP5UPdt1xD/OOl+mHbeZSjrko/F5mFLTXyWbmuJkMstN8ffVmPJW8dB8SzL5EOyBr1lesjYH43gZMTWZaH8pvZTn449v+/N8V35fm7tz298f2nlUyAt4ZpYf4KHVP6UoS4wxbKxdx9/G/y4EaY7+sRshP0CaloIpU+Gc5fGZ/7gdU0nEXvNJXKdDo5GxOs7JC/EqyYsx06sPkA1S+ukNFGXOYHQ+pr6x/mXiozFH11vC5CsNabNVY2pxeJI97SuE0RVmWKwxGGt4av8Cc0f+qxwknR9Ga8GFMYaZLKkHH3f2H2Y03aE/GoBS7Ni1g0G/j7UyhH7t9U+BSrdhanoHAVOKm244THd1mcPPrpAO7+Xue/4hV+46SKs9xTc3fpPBRpfB8u0Xv5+XuoF5UZJEnsiX2MSglQ5j/4rReExZiqlRVVU0s4xmo0GSpPQ3zhAn4tSYFyWvNm8hGcdEheJw+dfs3K3pRFM0WimXnfwJXDTxbRaxMwV85JqMf3Z8MsBG+N1v+/PLvSYibFuJApOhND+h9oUhPo8MN1XBYhSknCXNQHG4/ObuiFW7ivEGr2B1tctpf7ZmObnRGBOahxLlUGv72JGph2Cl8XqWNEk5qX6VUZXzF0Pxsnn9C2skyaTGnvJs+a8ZLPYZjcahlBisnrvrVNahELHPy595iPUDl5O2ZplSHpTFYVAYwLHR69FIuszOtpmfW+DyM/fjnef0qz7BrmNvZ21tlfX1Loerf4V+ZMLe+zqoiMHS+7DWsfeF/5XPl2IB7B4TWZ/KyHRuZQx58kF2uCAVAqx2V9jYWMMpg6k8h6Y+zPHRDA3XYnFliaIs8LyyxCFnnvYciRpor9ibGs4OT9Kmzb+Yu4Xxzis5OXrwJWFqdmZn8JJRdBZ3Md49oh1Nse90m2w/3BMMlx5ZEG2o+h89MWoKR4qX/PKO1TGP75n5tn/+rWUrs+3v5fdvLDxbM99g0g7xzK49SOEnpTxXK5175/GrfwErdwNg5x6gqkR0syxKnltfZ3V1TbIU7/EcwZ92Naaq4ShgSrLneui0MBfFlFIwGuX4MAs16PdrTC0mv0JZlgz652NqrbvOV+wXUGcjTGUZbIxIb3sbd4/uZMHs5gX1HGf7h1kaLHPVTJPh8zfyZPIo42iFxmXTdC97A955kqIg0lGNqfZDfxOyVziGAhVRVZIBLy6dxEww5c/HlEfTnkzhAxvHrqW/Noden6b0FX975td4cjxDo7GJqfXWX170/l7yQCmrAiw4VYm6sBdp+tF4RJ6Hi+XBWEsaxTjvwoZZCfMgvM2pqSmRvHdG/AnynDTLaDhJ/esbakQ911QV3byLtW0Z/lKTj8u2DKWe2/gORlfei4bZaDhkNB6FBzUcJIF6N9Gxcl7UkifRg/SbytqSVClVT6pWVcWyzhgOh2EITQbeilyiHOcVOjQSJ0JwOshvTDjpk5/jIs1wMKSIi5Cee4zREhXZCms9qqzQuqAscsqylAwzlNitsfiyZDzKUSqmKi39fs5CaUOabXDWSKklqLPmec7a6hpZKvITppRrMBwOqMqSwWDAWrfLYDgINV15z41mizIb4ayjt74cSmOyccj7NkEeROE6pqZKgpANqqqSz2gto/EYlKYoDHmZh8boKyT6CGuCqUhV+FgGyaqqZDySxnCejF8SpjqdqXAdLcVgE1MOi3VqE1OVKAuYqmI8HjE3P78NU6E1uXmVfP0jvmNrMrg5HA1ZccuhRDUJzuTXaPV0/cxYJ0GaCt9TVhVq5TQAZny6DtJMVdHv97dgSpFkqejjKXBe4a2tje2cFZMy8C8JUyiZsZKf9+KYKsuS8WgcMOXo93OmK4ONbcBUdR6mVtfWyDKNWi5qTJVG0cwaDCeYWlw+D1NKJ8FEa+OSmEqyRn04A5RlKeKhWuGqbx1TlzxQinwMsSLRRh5KU1GZkuFoCN7XtVnnHKrRQOWKqto0sxE6acZMZ5o4FqmOgydey+rUY8RJTJomnLrsDyiLElMZkqFwwvPxmBvKRYpkH1koo219/LZyyb9TT/6EFODCZPqD0zmL+QojMw7yDHLgSlaymbk478FvDqQZDESTT6CwiYPYQwa6V277XD+886drGQ3vPXnyfpy2JMVHwnvabOgrHUEAm9aK8TgnjuUadzodWraBNY7EphhTYKsK78oaMDpoRoEMSiXj36JK/zk3Lj1NVXp6ViiSzhpMlYsOUiV6U9578qLk2NyHOVIkLAznuX7jAyRxwtraKjvyMf18wPp6j3GeM5GKMKZiek7hwvsoB3nda3HObZOb0FGEt67O/vzknigdmvQCGmMdLgwHCq3ylXWgbMNUo4GxFdWgYjlaAe8ZpcKa06v3oPY+elFM7fD/iIiYypQ8xx+xfvYyOkNN+8q/5NkzP0pZOkzluXa0l8d2P0c+HjMcDrml1T4PUx7PQ/MNspog8h3CFNRDeifWDlOueZZWP8ZonBN3XyPqADNfxXnHiRpTEtDhPcn6awHIp75M3J0Lr7lDAprZv5XreO48VyCKTDBVzX4VZx167R5wm3tLsn4vbvorxGvfJ9IkOx88D1NKRRjryEfjl4QpE7ICrRTGeJ577lZuu9vhTIWpcsoy34apQfOLnD59L+try8wUz7JjYQdJHBN176V15YM0Du0mPTui3xydh6kkludl8CKY0iH43cSUdHG0jgGDXv0+GLdwex+vMXXV2r0XvacvarBFnAQ5i4SqyMVm8pzIW35QqOsqBV6hvFidxmmTJJX/X8eaJElRSjEejRgNh2iUuM1NTdHv98mLgv5gSHfY5dCr9nDT0pBWq0UcJ1SmCrXQikaW0szEKGZysH2710avx+LSIg9OFywfWRHTmpCZCHFGh3mOrf7wflu0ZY0JekqqfswnmVYUVHtd0AHyaIpSGoMNZBLaGsPAvZtG9dubDT+1qY/lkcitKE29+VZVxaCYEvpvqkhT4d4XeYnW4CopyalIpnEnMwqEdwEerVwtWFiWJc46Nta7bKyvSwnPy8NWlhWnT5/ljPrfxQnySMbK7K+wsdGnMiZw6y1KK+IkQRFhrQ8RXIVSEx91oWqqID7lrMW7LdI7fjJPEJGkKWWZU4UhSRWIIpt1nVfOOrM/xz3jyRLPNfMxdu4BnHOc4Kvote9DK+H4V95Qji6Mqav0z7B/5xJJkmKs5eQoxc48QF8rei940nZPJFimpjjdP04UX4/r9emaL7KyvMzc/Nw2TJVbMNXIRKBTf6cw1e+xuLjI15Z+je7haymnFgN2/kLU2fuqzgi8n+hjyZM5yr4gTNBTdzOe/ysAovV7Oboxwi/dgAJm7kjZ+IbsW/sPHMKjtmAK3Nk7pL9gR5ujj0ph576GzS2u9SU8jsTE2MW7KLxnffarXF5eR5o10JGmF3e/BUxRH1pXHjjE9PIK1e57KMuCXc19PF59mDXVJe+cgcpTTv8tJIrhac+ZM4uCqewwc2aWDf8Q46kxsdqOqav8LSgjZI1Hyy+dh6mruq+VZ2q+xwvJUSbaeDWmItmvyyIXhu7Io07fwhUDsUO+FKZexGArJooT4mgzvZz8UlqFU96Fmy3+HVqJjIbzGmPgipW7yPfmIYKfzLCkYfLTMhxK8yiOYzZ6PfI8D4ZdEUsry+QzU7TbMe1OQq8HeSEbTxI7bl4saLXapGkmm5qSCCRNN6dfL7Rkintzs3qp67EFx6miZOnM2eBZ7muarzAvpU6tlQgcTuK9SfbigUjHeCf9D6WCp0HYJHUco5xDhY3zS93f5/Wz7wnRqVx/URuNqfQHwbsw/6NJyo+GqerwHrTC++Drbh3/oJ2jlEzgprGkxzpSRFGG9yGFr6ipy845fGAPASgdfO6rkrIowHmKcVF7v/uQpZlQuwWotDQjxYxLDjfxOZerNqFKb96mLRkXm9PdqAmFXOYJNk9imNAhCcwbYyqMNQHEch1eSUtHMSpSxNFmGbTGlNpUoyhnvooyF8aUUQ8zGL6RVvNZPJ5r49tYSo7K967ew9BvxdQGg/xXsMayyzn4+l7stMW1W3Rvzuj1++RFTlkWJHHE/NwCrVabJGBKBUwlL4IpH0qRL4aprVTf25avYWV5mbNnztA4+4OMGr+PK+SwuDCmNoOwun+JJ1p4kGjtNfJEac3BK58MgYfn2t5t+KvETO454kD/TQOmtlz3HV+X/an7fXKfwnO3FVNu4et470m7r8N0DC+stlBakycvHVNq+S50nBIbUPoLdbm7xlQ+Ub6wl8TU3hfu4NTs31wEU/qimEo27uWFWBpDl6stqgmhAmNm/5ZKdyn0BgxvE9sDKkwi7DKlzp8P3LoufaBEwhrRetOha/LAayVOYZObIl9TIs0dIiprPXleUhSlbByBbqy0ls0CGAyGItcRaQaDAfk4J4pimILu2ir5eESj2WRmeobBSHo3ZZkTKcVGL8N5T1IZ+r2BfKA4ZsfCXLBZlf6Cqcw2NstkwnQiA735a/OzXwg4g+GQQa/PsN8PD+Xm1ybed/VEPxrnqX8JFVrkx01t7RnJgKCz4BGGjnYoJ7Vx0eeJQ7ocGmdaodHgxdlvcjBGelJ+JBwyE1JvaCgiNMWyrPBWBZ8ZoapGUVT//5PSnPMOb424CHpACaGgyHPGI8ksi2JMVRSSyU6ysZAdTKiVkxmlJAjreW/rzUBU+7ascDr78J9a3lC9uWz/VqGbOysMQwl4HM6CNZKdTbLGV9LSUQyxQke2/lD1gULoh/kXwZQRTMVxxaYZU8BUFDEYDKgCNXYwHDDIukRRTNZo0O2ukucjGo0mxe5WwFROWY6JlDxHznmSdAumkk1MoSe9QNlg6nt2AUxtDRYmwcO5azAY0u/1GfT6+KY9B1Obh8omplT4tR1TKBnY1DrCxzIgK2X5TUxFCD07TpIQfEigrMLnxvtgFwBe60ll+jxMieKAq6m/3wqmnDUoHcvn1NLPKPKc8XCEBsp8LP0Xa5jUdS+EqV6vR9ksLoqpbeeJ2n4tqQ+S89c2TKnJnwlBoztvnzx3vYj0ikztKiU/RF7Ih1rd5N0SosDwlr0HJRRFYzzD4Rhfgk7loTfWMRj06wHJXr9fN4W0ks1SaY0e/zvG8ftZ3+hJ5KUj0jSrKcoKy+N79tJoVlz1whM88o2nMcbR7rS59863sLBjlijSjPOcU6df4MTVM+A9VxxZ5/QN86RJwtUnxiRpKlz/MP2P9/VMwLnrprOWzgslG62MJIohSF3gPbZGgiJSiqtOPUtReoZjS9pIOXXljTQyeTALbLi+CURRENl0QDhoQo33B+d/UhqQ1vGXZ3+bH1z4yTrSmGRG8hR4ivh9pNV/FECFPo7WMuy0Y2GWT/FuiqLitd3/k7wqQ+rcoAz6PJNIeLKBK7wYkTovmm5ONirnK4bDdVpZxnAwYjweUZmSONakSUqaBB+dKBI7XlvRabdrz/fxOKesKnw4UK1tMVGt9nhMuclxt0qh6o3Vo4zw7ifBgQkluKooiGLZaPCiyuucDdHyK6uHEsUx33/QkEWOauhqTH3S3cePtz9BjSm1HVPx+muIdh8STI3GnF49wfVNjYrUdkylfymY6gmm0o170dyK0ZrxwoM4/xeoXKFLjf4bHTDlUEEg0Pf3UDY79I3m0UeegeV7aHfavO/H38WpG1bQkSbPc0ZPfIYqlDonjoJJktBqNmmOfoCNG3SNqYn22J1r12+7Fh6Ym5ult77OE/0P0+l/P2bhwYApFzAlT3m2/lpKE1GUnn72FVr5D9FpKRoZXDHWHI6l9xQlCcnoDbhAYDmpNftnemgFafc1sPsbYZOU3oWf/Yo87x4ut+/m+dk/BOAqd1NdrkbJpv5Xx4Thev1CxDg+wA2XHSQvKppnprG2JI5jltt/eklMxeHwtVaxOH8PjeE3WTFLNaYG+Tr5eIRfvpM41rSaMWms2Eg/X2Pq8qW7aXea3Dx+I3jPk62/2oapI+5R4ijDKXMepi6rHF8cSqCwp2VwRmbUJlbFLN+FX1/Fb3SJYnBqgqkCa4Jtgr/4sXHJA6WmGCJRkzEyFCTR8mY5oaoskZa0VCFZiMfikCZxmqS0Wi2iVKZ8R6Mx1lTgJeqtM4Q4xnmD8grvLIUtArtMNpBGs0GsZeLXVgWj0RDjPBu9HjrJiJTHOcWX03Xi8TpVVbKx0aM3HMAhuRhPRZroiTOgFIfCDWg2W2RZg0YjZTwa1dFcnGahbCSHZjldsrYvxawYjLVyIChVN70IUVMcSVahdCxSCRqMqRjbkrLMw0CUWOaCqhv3E5BNMqm/2fgDXj99P847klikzl0oh0246hOyQBxF2OyDUvYb/SZxLEyePPoZTnUjyo3flnuqwGQ/i0FB/juhnORCj8KFAEK486JwqlE6Jms1qWzJeFzgXEUjjjBWyktScvKU5v9j7s1jLbuuM7/fHs5w733zq4lTFYukSEokNZESBw2WLMlT0ml4anlAOm3HRpDEDhKg++8EAQIEaKTbnUYabsR24nTSluUpdqtjq+0WJUviqIGkZEoUh6oiWazxze/eM+0hf6x9zn2Po+wAMQ8BQfWG++49Z397r/Wtb32rHWSW8rf66BmKPEtKP5nfHULAR0/wvbTaQ4hzryktEXk8cCDozIqv13yrnZMjQ+SksJmhbfus+u1FeSllAJey/Cfh6j3QOdyS44Wzd3D6dJo02HmKnQdksz7yONqoAVPPhW9x19GW8ehewdRVw2z6+piKMfLs9i5KKW5caml9mGdAr4upGS5EZo1G24LZde8nZJbPv3KRpnqKrmswZx9kd29v4Nz7Ln6UItu+D22+wujrY4qypCxyqkow9cKJX+cm/Z/IwQ+8nP0ubTVjs7qK84698Zcw3etjyo+/SFAjfLDkbsSp2pI3DaZr2W5qKuVRSmM7k+hPWR03LO9Sz+SzVuPHGfsJrpNJhdYaXCvR/bmzd3HyxifoB8356LBGxmajRMb9iZuE8nl5J2BnY7a6LSKKl7PPYPY+KM9373241UffEFN+7VH01sdQ2nJN+yydgio0xNBxufx96Ttxnmbpq7D3ALOFh3AZ9L2EUuOQtf7KiccxxlBM80OY0lfv5dR6RfCerfDlQ5jKrOUHbrgDAGu+TZZse3qc3LCyx2Lc5entw7S0tUZcguFNIfUWA7YSpxuCdPaGkIa0JJinvHNQX4RI0AFDJESRBPffmzsIy8NxXZvop/kkMpVOw/4dO9PKa6S/qY1B5eJD03QdOzu7mFnD6u4enZM6g3KerY1NQpAi7ayqaNpuvvh7Xh5JQYUf3hNbhSyjaYRyy4sRNh8liaUCBYWRhWpszmw2Q3spUPW9Jn36HEKQqYTR4IMsQu8CgcSVqlRIdweoRLnhJHaJXgo76OejdKlK8T2mQ3tOMwUj3fJaKXJ6a+tIS0fTNtRNk+iW9NpRun9VyrDmcuQ4HOJDL02AgCI6j9GktDqtg3499L8bJNPo/75S4DpHYzpMchg+yF2njz38XdM746qeLx/Y9FRLePV67n/q8OHTf+vN0vO/jUtYjAOY8hzAidy3AVPEpGgS+mbAFEGg931gyjuReoOi7VpcGik8x5RG5QXG5AlTO5hZTeszOhdwBpTyXN3YZK86T9c2lFcuvyGm9OZWcujdGzKUHlM6XKQ0l1EJU5fsxl8LU5gC3w+KCul4dR1t29DpA5gS/pMI4maNYKrTHb6YY8poPQREIkEPtEoO5TZ0BOMHTA11RUSuPp3NyMtRmk0i66/3wuoFIW+JqahkHcSAiC76HraQqOY3wpTMhmq6vz6m5m7TJDZEDRM8U+ODLNS0R0U1/3dPi7yZCvAtnPPSzGJ8sj+WmklI7zgkwGqtiP0N9QFjfHoAMgest2/JEp8tozcduRWblRACgT5qmnPILu/obZpjFG6cfISxBXXTMauugjKsbmxRVQaFxhtpCGzqXalDHGjkAmSMcLo5Md3Upm7SAuwLuRnaVuhsJBLL1A+wsjACZSjKMVvb2wPPTdokdCpYheBpnccHR90qylzjnEfhhx6UYe9Tw//MXy/OF41zEk19ZPXnmE2ncqCkn1MJOCFE2n6RaMWqhX399+Vnu5oIdM6TaUOV/YI8hRipzT8ApSi63yIFrKLL14pgPCG4ZAHjadqIwlEWltwmg8FU+BwCipTJSm0tDNx31TQ0XSeRpg9pcp0ePI1AaIAQ/EA1xoOLOB0WhftNUP/tsDYPBisqDYA7eLi93QryAEQ4t7VARs21ecCHvm7S9xPFAVPdyiN4rTB+PsdDNgno/PeHqWrhy7RX70Kk3amn6QCmTna3M84WGJkxjzZ/zqzaAGXQdiKYUr9ONIp919ImTNVvhqnJlxNjFcFHok+Yihn6Ss5m9i8oCvHrE0yNQVmKcvSWmCI4fFDUndi0nznyZRQ1TTPDbs2lrDeEedTRXBzzpX1pczh1Y8utzU/znP9tmWxqDG3qA2u6js8/Czcv3w1EnguBF3YrPrY4QSnNpeu/KUGi94TSs+thzIRM5zLrsD/EQkBv3otbefh1McXGB/EkTDUtSjlUCZnVnPSf5tv85hAkdsuPoOE1mDqz/jDee/Se4PDa9qfZVf96wNSZ3Ypz+4auDSwvH8bUmfWHhud2vfs0qLOHIrQHzxl2twzHtR9qPwcPQv0mhwm8FeUFxOAJifMfJK2xLyKmky/RXyGKLNZoS1HmeC0/30QoRmOWVyfkr1wkdik60oYiz1BItB18oOla8C4NFurprjj8/RgDUcXBgbffVHWUgl5UQr0ZnZoB+1NZjlb6ZsxDH1IndMQ4zGNBeaKf0lYz+l+5sD9vXOwVREqJc+7K8jJVXVNVM4lqiBJ5eAhBD9E6HC6MxV4X3n89vSmtFaUtybMc5xxf2vg/+Tunf2WwrSkKmXLZb8R1XbO7u8P+/p4keTEORW2tZeiUFAkVWfsbKKAtfkkyMK/Fr00roaDSxmYQus85z/5uw2QhkmeW8UhqL1YrMqOlzhEh+ohHGsR6tU+McpjF0KGUqPxiiEQVUGreUCWUakfEM1/hSdmVIijxget3MYaOaR+jFFAjkIqk/cb8druOn7FE41MD4l0E/xTBB3568xzP9Q17r4OpwnuZeZEwFTbvo7heMKVfPkrkLwZMTaYfTWtLIv1bVqRz3G3cS1h9ZMDU5uo9nDs/YdIucNvYH8KUNpBvfZBu5TFiygwPYirbfoCTqWfh7JGHB0zZ7ftIKZZ8IQVHSouisVt7hLaqDmBq43UxVe59iJWlhKn9CmUUSi3QecvpmSIUcOPm/WhqnmtnA4ugULysIidj7wyA+AiiuG33o1w58X+TzwRT22v3cJM/QpZl3H630NwxBSXBez6UHeeZ809JM+7WfXx26VqiCvxqgPPZ7wmmjGCqXX4YBWQ7D6C1xevXx5RffQQXC+pGs7fdsbDIIUy9Q93Bnq7ZQPpoXg9TZ8++m7ZtueGGJ+Q+pgM8qsCLL97JypoIhAS7hzF1euN+/OopQIl6K1FlIFlc8JI5P7s75fajy8MzPF55YVveQk3+5hmK7HLEON8c+nVySCWQooiYUmljZGCWDooX1h7iZHsjVV1RVBrXdWRZTp7l3LUhEWqei9X5aDzmodE2TVNTmX/A2GbDA5ao1pBnOZnNGJfjRA8Y+d1ocU52lbIcoXXA+1YW6vA+U91iOHGFLzUmCQGUTkVfOZhkf0pZmUg9hsP08C1SrHeOy1lG14nixFiL0lnaEMW+usxzbrv4v/PdW/4rlNLcceZfkFnL0zf9F4Tg6ZpmmMsQYsAEwz0XfxulFY/d8ss8feo4XeeZzWqMSnSVVYBBjRb5xMIys9ke00sFKl/EO0ddVSg9T23zLOeezYm8v+YP0FrztYUJ0/29YWERI7X9BRY4+NzjoWdujMGMLJ0DrfdQimFDiFE2hT5j0ChCArtYXmRD9Hbg0aC1ZnlpVYZFaVkrPYXQti119ouHsrhU6ZzX4PpYIR585bfZdQBTX3n5adYPEHW3rCzA9v3p4JXayYApbQdMRSLT8YNU9ScSpkr2jt0v0erlVV5wFbcfWU2YGpHPvkvdSLb/crbKjeFdxBj5wvPwjlMdo1FNZseHMbX3Ufy1j6O8SFLLsiTf/SDedYTgONkXy5Ti1t2Pcmb1IcHU2qPECMXuh+hdoq9f2k6Ygrj1AM+vfvU1mDJb90GyPtFao4zGHP86uqphNiMqDWaMDhkXneH05n1cGReUecaRMIEk5jFao45+jbrvu7j4Pk68R0aZX3jesLL7IXJkfV6zZbnmnot0nad95X3EtUfZXvsAoFjb+hoz9QxHV6ZMZvvsPn8zv9Bs4Z3jcl1x8+qdXFBn0Uoxnn6UhUVp3ua4QivD5fBaTPVBOL38OR6mj4yRvr2ug5vVnZzlO5z0P0umImf1vxowdfLkk7jOcerqAygUNnuJbF0wderUU6ysHUErS9d2bG0extT2yjP48DTee47Pfpzr3c9wwp3kqLsOH7/BD5xy7C57pvueS9VBheUBTP1/q6Gk1zgI2jiPrhkyFT24mVubDwsFFM41NE1FVWu89xRFjjGW0djifIfNcopyxMraGqMgckdlFyiKoqftEu8vfRxKa8ajsRxiylDsl4hOQDKYcjwiLyzBC6/cde0gfzXGoqJE9ZKOg81ymRdtLK5tcUHMCUUW2Kd8fW1HHbqnUpTMAI1MnszlcDJWaBht8b6jKEsmE8vq6joLi8sopVhcWpUNPIoAACAASURBVCHPMxaXV3Cuo7EVpmlwXYt3HdppRgsTMpuxsLjIkWPH2Nuf0biIbx1Gy4AlozV5WbKiR4zGI7rtElOO5bN0jiDjAdFaMxqPWVlfx5hsiKAyIxloDP1njINz8MFFNHeEEjGDMRk2m9OKeuDTU9YZpTnWWohB05f5TLIc6bMNlf6eMZbRZEEst7WWqYtJpy8cuEmbsdCT1lqyLCfLciaTCW0jii8X/YH3/fZKUw5iCjW/o/PeLnkGSk7YA5jKpI7VY8q3BzCVUYyKhKkFbONFvViWrKytE9UkUYKRhXKJxbgEwNpaxsqKDHPShzCloSup8ww6EYGU4xK9uDxgKqs7iYoBkzLgQ5hKmbQ1ljybHcKUUt8PpnqHZYM22YAlpWWEgtaKohBMZdbSdfLaNrPo5VV8EAGR31ugHIHrWnIrsuJyNBLMG8ORY4G9/Rk7VwuaINpGkRbn5EVJOSoZjUfUL5dYY3CdmEz271orTT4as7q2jk4jm7XWbE1zed6vh6nEIAxn8gFMZXmBzf1QG+vdhjPzWkxlNhtoemOzV2FK1KJvhqnCFVJDU339MZJZm+rJORO9QFs30rmf2hbe6nrTA0V4WKGYjMnYO/YAXduyeOVR6UMJ/U0xYjevAxoYTxbY3tmmqltu3voQHPd0bU1daxSGOzesKEMMVLOGvZ1tQJQVp6cVMSq0+T+waSJdWZZcc+IEWluublzl4oWLjIxhdXUFjGXzyANsPX+JECJFnnHvxr9maWkBmxmi9ly8ej4V52WsZqk1ITieOPnLZMZwz/nfYjQaY7OMejplWs/42nW/IM1cSnTsQWu6VEOK9LQVmCznqC7ZmTbc8PL/ig8yPtOPP0nwYh2vjea953+L1dUJ5doRbnv+f8EHR5MZ9vdmHP/m/zAUCTWgOsd3T/1nfHLtFxht/w5ZUWDynOAdbdtQVRX7O/uMyhKbSdrdOcfnRg6TK37qiiHwGwTX8dS1v8Te7gYxyCL98O7vsXbkOHXbsLW5hckNLjh8kJ6TECG3ls/T8t+f+7/YsT9I39wY+00w9RNFlTYfpUGJmiUvcsZjETN452lqmUsiUklPXVfSZ2SEAugDkr4/4NTZGTBLheRIURZYZTh7/Tq7ZkFAJJVt1tfXGZclaxcit91wC4/mU65cvMzm1iVEgm0GVdjb5bp4Y8v1Z6XYaU3GS3sVbdtxclJIJpeCNL/6GJnKB0zlJ77N9k5DVbeyKUVHVb8DbccoNgjPlSijuXq3o/tKwyX3x7APzfJ1rIe/iy7gfP77ZHbMK/pFyrLkh+48zs36PVy9usHFixc4Gn6UleVVlLFcWvv3XHmhI3jB1PLqGosnr4ghpfeMvn1SMNW0PO8brJGJnJ33ZMZQXvvUgKkr0ymzesbR2Y1kWnHrzkcSVRvovCdEOEcYsnmTZYyX1tietjgX8eQU+QjvJaj00XPu2CNcY3+IxeUFijznypXLdMFRLz+M2+8GP71cwbHq29A5suJurDGMFhfJihLftoQgmNorvsD+5j6Lr9TYLKM176U79jUWm4+zqBWjyQtSOzaWcVS8wF9ShBHaaN5365j97DhNI5i68dguZ/Zei6nx/kcpl79F6+NghR/TqdJj6sWdFXZ2c+ASoBKmMo76v0NYe2zA1IsvvptvaKHm7lXmtZhScnjaLCNEMfo9iKlxXjAr/5zj3U9hTZbYhMBT0xNUzQLhlil/7+iP8uT3HufKpcu8tHWZ60JEJfbpja43PVCcd1gl/Pb58l1Mt7fx3lEt3DUU3vo3DgplZKO93GR0ah1fKq5BTuwszymLEhQ8cuf9aCXy2qau2d7eoqkqxosL6DVLP2N+aKI0moVsTF6UzNQCu/k6oWuHMcIufIdbuzHBSwPf8rjEKsgUjEY5e4uLZOUI17ZUm1cJs4rWddz+9D/lO7f+KrOtLdz2jqhEQqCNgeBE8WG0RivD52PDx6MhPS+R/qaQytgMkxXk40WJlLQYJNa1Z1YFFicFZTliMp6wtXmVS5cu0roOW+R0bQsHun7HRcmkKLnrpd/gf16a8EdrPwlaYzc22N3ZJgSpacymFft7YaB8sixnaXGB0bjkn9/yn/Lzj/9jvHe8Y/rP+OY1f1+GX2WWyWQitjfVjHo2gwqK4yVra0eFduskg/zF0YMs+SO0boHGNSglnfbWJkm1zWgDoCRqyoucyeIiC5My1TGawQ5iMlmi7VrRsntHXVdS4NPJJUBJtNrWNb6zRJQY+BmFn9Zorbj2uZrmPaU0qqVa3u7WJtPpPhevidwYA7ddVpzYKngwyGsyRMFvn8u5jue2O6yqiVXFdLov9Y0cvIJu+SGJLmeGSlUSCWvFhYstXdvigwJTku18iNE1z7AwXsCa89x3/DaUgivPnGe8OGb7/Htp6gpzcZG/yr6ONoawvnkIU9sbV3mueJ5ZVbG7uyuYupx6qzbuo83PEYJgamlUUu7eT24NN63exKNLX6cdlbi2ZXnrKk3d0LkOu3EP7fqjtJdvIyrDqajZWP0KbQw8z7dQS5pi50Noo9E2Z32WXDQUXO+k6Jwbza7NuLX8Zc5uvEDXeRg/hm876sYzm3lWu49xfi3SOFgc/Rnn2xcFU5dy4qX3zzGlLjLeuo1JUTIqS565ss0Ni18Bp7HasPmEOYwp/hCc3PPsfM7S4u8zGpXoIzM2v3cz3nveYXL2qwcoujGjuMTk1IRRuMism2HzHaY7UOSvxdR49DyL4yN0WxVN18CrMHWlegVtLSjNi9tLZNfkLC+dYXFSsr+5iL98D9F7shh493WLabJpQzad0bwGUxzAVDtg6vT2A5w78qj006i7sKMSY+1gpfXexctM9R6z87fSHAnk4wUWFjuublw6JMJ5o+vNayhxXkCWKxVR0z+1mdt8hHQqKg3OS5OUSqnp0uIieZbTW2NoLQU+acPom9ogRk9VyQx5rfQg1VNKyQRAm+Gdx7WtgCWdlJ3zqJCjMWJb0rSE6FG6IM9KGfvp5OZrBbO2pelaWgJN27JfVUhHiHxCr5Q0CnlNllmslSqla8Q6W5RVTuoFIaSMShGDmLzhNVG71CbvpVHRe9q2YWtri6aucUnu510npnS9T5EPKB9QIVBlmp3tbQKS3sr91kkO2c17UpSi0RXBt7TtiFFRQvCEztH2irAkHey6lrpK9ineMy5LlscFoRzjlSZ6GdVb5jmTKuDCElpV+Daide9DBpFALyuXL0i6PBqNsNqwuxtRdGAgy/OkbjtgpRJE7TVs+GmdLSwuoDC0naOqZulnZLP1gyhENouqrodRwXmeE0fQlCVxJss0BPEfe9tdSjj8nmYQTMl91EkUItZEkaRJwPlWMKUNqsdUzoAppcEYhdSIA0odwFQtTQyh2B/kp0ppXNslTDnBVFPPFUHTfVTm0RFUDNC2xKYBVZBnNo3SdbiulZG0CVOhE28wVVfYCLtBsVfO8ErhtCJ6jeoc1mbkuTQ6e+/xSqzVtdbELMgt0ZoYHa6roJ2J2CRECAGtwHlpuNt+FaZU0wwHSogRnBdLoxipZtVhTOn+WUDbJEzxOpjKE6ZclwIpNbgTdG1LXb8KU2byupgqypKVkAmmuu8XU2MWl5bY3dlFKXH7zbIc58RySKfA+rWY4jWYMruW3gbLG9lrYuil6cwxlRnyPKMsS+pyNNQ83wpTb67ySgu/n8JnrUHp+el0cHKj90EUVVqAb4wlRsM/O3aK3zt6BJsp6mZG09YYo8hyjTEKKoexiqKwjMYF+1e3Eh+KPOgoGY5RGjetRBP+0HXEe87AY9dDjLj3PUemxNfKKk+7u4spczIN2iziupbp/i71bMrIWPa7hqZtCVpRNS37XYtSyfoiyEI+/d1f4+mbfoXPdVPyImc0muCcS5YIkeg9WJ0MIoUK69opzWyHYAwma8mUoTSR3ES6pmJnu+bqlcv0thC+ldG3w+IPgbpz+Lalrmfcdv5HqO/5Bj54bMx4bHonEPno0nPEkRrkw1opabSMmjIUvKA+yrfe/zGqqqLzDmOeglY2sP9u9SeoSmmutErzc5cWUC8iEYzJyYhoFQla8Vc2w3WOmau50mzwjuIsKo2WRQV80MPsau/k4BwVOcuLK/jO0egm6eD1vCiptSi9YiT4bgCPVgprDOtr61hb0DaOixcuULcyrC3EmAYKdUPRva1FvXTnpmZ8TYHuFFWewUyKnj4E6Lo33dv//756ek/qhAZjheaxVupQ2fTDr8GUX3sEH71gCgNKc+zoEcpyj7qpaNqaLXeOLDOcPHKa77781ICpqws5bcLUyf07OXvk0UR/GoxSuOlMGISdB4ijB9HJLWK68CUKtQJaYXE0u7uoeh+rQVtNvfAldtstaj1ltGKpn3m/HCjK4S/djVPSE/PtGLnu4t0opXkpE7v3tvCgLZnSnAmpMZbI2eAxKlLiWU11zq6Z0sy2UeMtjJ6QKcPC3r3kS5FpM2N7uyL4w5i6sfVJwhs5h2d/4cs0xlJvfYT9/Zpye1swleWMyxEkubtsmDEFtKnXoxHl5slrryM3Bp+C2ZhJkBaI7O3uUNU1hMiL+juMb/hRjmwt4VG0JieLEasiVivIMgqbM8lzrHdsz/bI1JMoJhwbv4dzOy8RB0w5jFKMipz3H8m4eMnRNA0PntOUseaetV2qeoYbd9j2MKZOuk/jfWDH/HPW1gVTXeN4xX0D3Uqt7nm+zY3Ne5MSTDA1m92K6xqm5hLb7SXKsiAvMlGNIT1MtG+Mqbf08tJJIfTF3ZuYVkcIwfOxlRfm6gXgizun06KYq4JspiEa/uHGi6zcfTtNW7G756irmt2dBpQnREddtYROClYutOm0TH0N0aG1YWG8wJ1XfpCXL12kqWYE07F+5m72lzumdU376A2cuKUmjy2GhrrapZ4FdmYFs67i8qVLKGA0Knl85xa64maxi9ea5Uvf5uXRR4bGowHw1rBy8Ql+Ws19iYL3h7I2JXwE9t0/xmxW8WunfpHoWxb3/pD/JkRGsWOhdDjbceXqObY76e0oixF9s9Z0uk/XCeebXpzMar5Z/zxtdKjHTmKV4qvvXSI6mUFSZTXjyRiVFD+KCMHx767eiDWWkycrnG9xrqVzHSEYiRqVZn9jA5860kNm2Z5OaeuGtnXUPlASsdFjYwCrESOfyMR7FjKHDuKoOlmaENsO34l9A9pz4asfwS2O2CxLuu42UWbVFT70/SlyaDqSPUyIvGILluw5TAxMZ3+Xzy4usbq0xKdO/Fu+tv1zzGabeNdw/+JnMNUM45M+XmkKm0Fb88xiyyu7Z1Ah0o5a7F5GZueiiLfTpY0l6mRIqCHLLSYo1PGvi4V6utzqQ6iNDxJQqZkRjJVScPCRu04pmjZjd3+Xz3/PccvNfwo+cOFKh9u4m+AEU/XSl+i6PZwLxI0HROyhBFPvvvV2Xr6cMGUf5dqVa9l3CVNtw4kx5LQYapqqYDv+CVlTsHfpC1w5gCmbZRy9/loA4fl7q/QQMVv3cl1IB2imE4sxNyWclCeAV2EKTbv2KM9VXySuNpRLLUYXlCoyomNluaE6+pfYxr8uphQyYiJ4T70mJpuZ1XD0cU6sOmLMMCrHZBnPPveuYZTu7e98HqXjIUy1dUtwHbO64pq2o2o7WtfRoZM7dsVTL0Sa5ccwWpNZy8LLX+JK8/1hytnI2fgkF5sxC4vfI7h78J0MwIpdxzM7X2UzjLhj626yzhHblpOLX8OHyPPbc0zdsfnh5AQfebb8Bi/b/w0TA6OoOfvsGVaXllhZWEA5Q6xlNLZSEZvPMRWVorA5NA0z/ad85+XHBVNtS2Zu5tLRh1DacHr7o2+4vt8yQ5HCkSxOqyMueB7cPCkLFJUyiY5i4w8ARb36E2lz1kBIBd04cPhKafb3K0LoIAYxmUuOyF0IOJdsyxPN1qf0s6aCAx25ruvkhljJnv7y7AIfvvYqmdE8eOEYphCVxqSaULs1keRFy+qKHpQXvbpB697NtacgABSjH76J5s/PCDgGzXQ6NEnNliEyLjKq6R55bslMjpoa/on/CmOlKK/5GcLl3+HFEz/HnvMiHy5HA6VR1zPe88U/oWlavnr3x/mHL/72UMDu6SFjLT/6Yk69PxXVlrue+Imrov7R4q48eep21t+ZE7xnf3sTNWkpMnE+VUZjjRjS7U9n4jUUA1obdut1oYeMZDx1jNKpD+DCnJHRBm8yCpuJ7Xnr6Vo3GDF+/fJx1iaKmQvUs1peMyq8zYle6k0GUF7ufUiumTrVrWIMYKU42FQVf/HSD9G5mTw3VfCt6j9mXSlU6PDdDN917H73P6KdTVG+YyPLRS0VArOmpTBCX3avNqD8W76U7h0ApGlv5f0W52HDv4e1y27AlO86VCsd7sGJjDwGwZTMAUmYSvY/+9OK4B1ET5w1c0ydfTfU3WB0SITA94epqqkJGjKjqJsaU+RCDdUt6yvL8mysODSYsIZg6seIOhLWHxN8XfcifTz7gXgnL9rvpM8ihWm7JX0rbuVhOTS37sOHyLTMmE33yDNLNsqp6hlV24BWjI99napxVE1H6zwL1cco/QitNe3yQ1y+/gmm+/s0TYPdvp+TQVHkOeWoJCrFmfVHMNZSThb55C0W13mCi5xPVJHVStSXi8uDLdD+9ib7NPgcXJZT6IOYmjKLO4IpY7B5+f1jSisp1jOiLR/DJUyRXssjmNry1aAQfD1MWZvLHtVnta+DqZ2uo3PdgKksBi7Yz7ISfoXQzQhdxWMXztHNppy6XrG/szdgajQaYYoxATDqb+jlZbXMwTAKCqsh07h+qEzq2I1IM0zv7+U6sYYgFexDskmw1lCWJcYamrbDtQ2Kg0ofOVBE9Tb3IVLI5jqrK3wQztAYi49BuHOVUZYFrm5wWvo/vNbkWYY1hhgCC+Mx1op19ZywS1JFJYZ9Rs9FAAl3jCYTzGiUuPvepiSthxQNdF7sSKJ3ybKloEPRdG2iNAw+KqIWSsdaSwxevhblvuS5yKzLMpfIOi0Im1lkoFJGXozomk4Sz1QPIQS0hiKzjJZWWFzMaJuKam8TuySbgokmyU4tEU3btsxms8G9tOmWRCGiFGiFC5GopIEt+nnjoVYKrw3KiGiibR1d5+bTJ7WmizBzgRjn3bkoI6Z1ycU1EBIfOzd6cKQAwsprdz4QXCPPOLOYaNA+EJRKm1FH9FI30EYTgk5dx0LRFkVBpsBHse5/O11WQVABoyI20+S5wbmI0wGr1RxTISSppiK4hKlUpzSHMCVy4abpcJ1gSifKp8eUj6AOBEIq9XV9P5gySkudRatDmJqMx7K+jCECdjxiLvwFFiapQMwgOFmIEyZmkny6pB5mRzJL3o1GhBAwRUHnPLWGGJyIXfKCqq6oXYsyhs6OaRpFVAcxJRYmIcjoi7zIZd0XOZmT9elDwNpMZM4JU6bODmAKiOKwUGQZk6UVvHMDpkZampgNgiljpfLath0zNcO7hKmj3feNKaU1KjUd143DJ0xFBDM9pnZDNzRRdq+HKWOSnY4iqr8OpqS7P3rBlNagjKg3X40plRX42PsIvsH6frPFv6QdVkdyGzm6NIKxyNRcCFSNp2odVePYr5rUDRpoW4fWgJGbFaJstpPJhDy3TMaygHxIN7Y/renrknOKqe/8Bdjb36duGsqyZLIkOvNRmaOVohwXzPYqvrnzAmVZcuz48WSINs90OjFNkg+W1n1fgyCN2VXpIWitKX/4Zjb++K8GqxNhtw5bERDFQ+m7D/8hk+vvIfgSyHE+0LQdisDy/h8QF0r+wxeeIETNn952L5cvX6ZNDqArq8ssLS1RFCU/9cr3eD7/z8UOO0qxXtnUJIhEt+V4TG409WMF7gNnhH/PLOPJAp98rqKedXzhljGPh7sJUVG3DvAs52flWXQcOjgJAWJfZBM7brGYcIOaTZ4DidsVcEynFa3zdM6D0rz36GWe3DxGi/y7FzlEIl3nU91ADcrACEQlz8NpnaTnCsYFUWsBRNeis5wA1E3D/hNXuPET60yUQWtPC8wApwArtIpo+bPUOOfR7o0VKX8b1y3n4PSNDbn1bM+OwM6U4D2vnGqpPugZXXyEqnWEmaLp5aWXPoDWcGZaobTl9OlHiF5cgPPcMpmMsNv34+oKt/IQrDw0ZCjK6qFv7IzWRBeT/1Rkb7qPu/g+yrJkYbJAM/4yo0JmFS00P8DuyoPkWU5ZlozHE/GEOiDScT7Q+YDdvh+v3HB4ALj9qSgQU8alteEZ8wRcuGeOKQVHl64BwNQ3Q4x0xTV0uuPC3h5dU3OL+nnGdsQ33a/Ttg6VR2oT0Aslx/ynCFGz08wGTN2wu8zzNx/nlv11inFJHEXW9sUSvu06XlKBbPt+bF5iwzIv7++RaUVuNePZMdqmxhpF6RcZn3iZphE2ZTQZU6x9VyjIhCmVl8kG5yJME6b6Quz3ianC5rjVh+kmI2Y7Fe3oS1Sxxa/tEzxMu44Wzy6OC9tLKKW43n+QrnOcO/rogKlvrX5FXjv+9TAVfcuz9rOsZatc59/BiVNPMasq9hyHMGVcxg3Tj8o0zOxvWEP5yNF9ZhGmPuBdicWRZZrClCwuWEIQyqdzgTY7xsbOlPNK0nJjpAfgn6yf5MNbG4TYELWiLEtikMWo1YEpZr0UV1sGp1Sg332886wsLSWe1HPi2HGquiIEz+pSycJ4AbojWJvhvcwMiIl7PqRUUwyHAcwPnOFn0kJvPvfdIUsafvxAtDuXo0as0WTW8IvtBssjx6XxD/Avd/6Y1mmiHkEM3Pruu8myjGOXL/NPD8yMmIwn3Hn3BxiPx3Rdx0tnl5nOZuzt7aXitxxwmTUQPYpkwRCDzBhXUH1hndHRmTRyhcD9T1UsLmU45/jcSZnGN/It2lgxxuubn5Sa92kMTF/fwCrPUapK4sn0l2cmfPjUDvuzKdVMbOjbTsaKmtR0JcPLAvhG3AaUJqaIKwymknPTUZQi1xqjjFh9R8RS23tU8GD6gnzL0vvWQWfoYgETO1zQtJ3H+Q6FRquAcn5wk+0tNN5O1233bvHOqxVTH9jYVWyertBaM7ElH2s7nj+xjveRzgf27DE2d6Zs7s44GSL56iJaW5xSfOtsxx2nNhOmClr1ME02RUcGv6U3xJQcNQlTyygNITiOHz0uku7geecNmu/5o1ibJ0z5N8TUZxau4cfj76Y1FPkj9Wl+wqVxFwOmJBPK+pGz6f29vHMGgGsX70xUtASap/lZ9uy/ZHPpT/ArKyyGCetn3oW1hu7YGbKtD5Lf8G2yLGN2+Qqnrt5Ii4bdVU5fWmFlXaZSnphs8L3ue0xnM46cu4sbvOO8FlXmHFPS3OeWv4re+AAoaLqWopuJIWsIdB5KmxGdo6mnON8xVuLEzZUPEMoH5c4mTN0U7yDGyLPq6TfFVOUqQvRMZ1OqqqLtHG0nNSij55iKG4vcsHZVAu3t1eSgIDORTm/cz5n1h/9GmFIqDJjSYfENMcV0xl5Qg2T/ja43PVBWbIcOGheER/ShEyO4GBlNCopyBEpTVTWsLtK6gG01Pm1Yc8pK1AnxQMZAoj1CDwAlSiOZJaTFd8rGoRsc5pLKxKWhkl9P9IFRkRN0hk6ncgwerwQ84oGTajKv6ks46AEEMk+hP+Rc6G1b+s/DAMceVForrNVpgJSk30tLC6LnT59FaSiKnLwokE85YDp1wiqMBq+8eKAFnwZTVSmtjXRNjY2e6DyN97RdS5YWatd5pvt7eCe/17Uts2omvjxu7noqG8zcUVRphTLz4VskUQKHP/Vw1vggs+ObtksWMSFNYjx0RwdKSw3PNd0v5ufW/E6C1UY6gZXGB9kE8R6VLMB9ED8ree8KMV/TKSIT+gclpqXi2KqSv1F4U83838a1YjsWjMdFTwga7zpi0o2WuWFltDBgqlxdpHOevVmNcvMJj8QeU13ClHhV9ZsPHMBUTJjS/YyfN8IUgqn0HyEIA2AylJJRCDEEfB+AHaA9ehz2T3lw1ZDvYjI7x33/9QPU8sGrNxTNrCbPZCibtYalxUW6RPn0NitFUQim+vpE/12tB0wZJT0gPnjKsqCu0oiEKMrLAVPB0eqWYsCUYzp9A0x5n/o2pA7WOzeTAmRlNCrqFLipt8ZUGkLYdU6shtwB19/0qSL9fir3rv+J/q6/msr/fjGVzJLpN6Q3wpTQ/mr+vTe43vRAyQiDDj0ET0x1D+9alhcnLC2NMTZDhUCxNGFnv8J6fcgZ8x9tvginxVrEJf3zwOf2byyZ4fVbvdZioWCsRqMw1ojEOPF32hjqWU1XP0wkELJ7GI8LvFegUnMPQntkn7oRrTTtX5wD4mAwON/w1KHPXJYlWsvfmlU1MqM9RdbDjpjUYEqhjGHxx27h8h98ifX3vh+lYWVlmTzPhrGdYuedU5QFv7N0HcwuyWv2FEEQ88TP8VXut3+PsizSou3rNoF6ukcZPY0LVM7juhb7lWtBa9quY2d7a5Ae+1R8k1rMaP7h0oHSg1ml969INtVJ1z7ckj6STOvtnhMbNI0fJjT2M0tEPNnzlv0vpwaKfrOCIdscYCYjXZI9R47CMgs1MXhRwiC0lfcB5x2i05/7sA2fKfZ/tofYgevtdZ6QxYAmJEyJJYgi4F3Hk0eO8gnzAYzJ+NZGwC0hmDKal2PgzNY+KMPp5cDJlT2cs7i+Fwn55GbzPvzKI+hkVz5gKjXQGWMEU0Yw5RYeEo8wbfCvfAyjQMfAbHwtNxX38LL+PXHK0OkwShbjuneiBTJvKDc+JHLVoDFHDSbK9+32/WTXPin9JTFSrT06HIry+7IglDvFi9nvcmP4WRQGskymrqbZHysrS3yrlAFT9/hPY1afYi/PhsPiJRVpNJyMcP3yDkeXNOPRjLZpyEaGMhZcuv4Jjp67i5vW9kCBD5uUSxKg/ekLig+X72cyESujZxa+RLOtB0ydee6dnL75aXrTTbt9CKKcbgAAIABJREFUHyfH70HHjGLpz/jz595NJHLTLU+jjIFohcP36jWYMtv3EWPkBi9ZouvmmDp79GHoM5j+oSYhwa0b94mfiqR+nN58IFnrR27cEC+3M2sP/bUwdbCBfN7/Ambr/gN298xxHPun/vrXm6u8ghosqK1WqCyddIDvatpmH91ZmnqfS+7DvMIuXPhtuuUfF45QRf7xynV8fmGM0ko43q4j4OeHieo3iBTVGoM1hrIQ75n2z8+SWcPk+LsZj8oUsYulSdPUQre4R5h+/B1YewJjrMytN7Kg6393hqZzh4YK6YMU2KEPrLBGVGBN29IXNuVG9je2f9uKzIrZpP36FjtFLomTD0wmE8rRiLZp6WcYwIFZBQdidrHxloKd0gU33rxP1zlmsxkvnVtnf29PJL8x4IwFAjaCbyNPXvcHROCW73wKH5WMDDaGLMvkUPee+765w1ffPRb+1qQVocSILssLqu0ZZTmWAqNShJAaD2OfXckCDN4NNEXfq1PkRbqnzeH0Q0G0VrKECPgBA/Lv2I8oEPFGsBo1KrDZiLjd4JUiGuF8DVqKjmG+RvraV+yVLP2pwnwmiu8pgbcZ5aWCAieYYv0Rju1tE2Ngc/UDuK6h7aRR7Xi5y1Mv5ezs7DKb1UQFTdNJj1CITBZGCVM1ru34/fCTzOIMViI/ycOJ7juAKasHTBEhs4bl9ge5eeVWjNa8ZD6Lnwaq8ZcIMXIk/jz705pZ0SY1V5poimRBXSfqvhgjPxF/l7AKKDi/fT8/s/0SbkXWet9fFIIXV4hDmJozBEbLCN0sqafsQkHhRLkXQmQyGZFd8yRt26KV1C5uincwYsR3eIbrA6nhkEQVKXxQKJ1TliOMyTgd3sVf5Iq7M5FFqxhxKet58qbb+cjsMbLJLjFCNd3HRz1g6sabvyMSXe855W8njm/AOY9OmLppeSwHc1ZQzWa02R2yF2VPvwZT4cjXRf16+YOHMbW+R56J4zGhkQWTMHWaO4h5NmQqBDi7/hBKa05duT9hKnDyyr2cWf3qHFN2RNx5K0yR+nDiHFN9lBZ7ViDgHeI+/zelvGZdYOYiVSq+xtYRgxNDtNxQmIBRHaNMikYxWcjnxspCVtLVuz+TyX77syk7e/u4sU+nYUChE6sIyihR6GQZX9q+mV/qNhjf+0Eyazj37GPYxR8EoJ09BGiKsUgFrbFkD29Q/NBpAKrPvzCPZEN/YM1P38QGDCm5bO+yKdXVjBjnQ4+GX+qffP+VCMUnb8IajfvCOWIElwb1xAhFngtF4D0khYl3XlQ16e/2NRrvg9ShgifLCrKswNqMm98xY3d3Sts2XDh/BBAgP/iugv1dz+pMCn9ffFfOx59JIwZ6w86U+fz72yxZkDka6H4jV1gN67PfR/ufQcWcGKW+knJe6VJOWaZOxoJfv3SM961fHtLkXukWQiQvpB/BmOTzleZPG2OIUQwEJdrphzulrmAlWWNVV5hWspD5/ZlHsBLlpS7ufoqnViht0PHVnl0RjSYoOWTfTtfMBb68lLPlPN0Fz8bkvcTgOHLxYe43n6RYzjA60thAWP0yXbNP1+wz3v8IuRFbjmznfp6qnsAFx3Q6467lT+G+9zBNXeNXvkpUGo+ML1BGUZSCqZs2H2Bnaczt18gI6OD3uVD8ASCP3a8/SqEFU1vm32KzjJFOIpq+LpkoD715H93KQ/KhBkzNaS+7fV+iZQJVNRsi/Xggwj3pPj0EALLBBS7OzqU1JBvnQUzlRU6IgWfDt7A7DzDLJozKEWrjA5xZ/zepL0QRtn+cq8vfovAF3nd8QP/XUCi8+Tr/wfGSLxzTtE3D+5/LYG2Hruv4R+E77F9dYmq2iRFOXb2PXzt+Ez/BZ1+Dqe+5J8iyv2KiVzA64yIX6FY6ikwzyjK0n5HlT6B1wQ3tbZAan73ynNmeYJBgSB/Z5PyVBa7RMk38xd0VdMnQpjCefgxrhb576pbA++0uSiueOLLA6jMWk/zTXsk03fJDAy5UNHNM6bfC1GGhkd15gGy6izfta+qrWkHQsmO/0fWmB8r53SlTZZhFWVDRpRurI5lVZFokxbmRwwH6ukLiMlK6tL2zS9d1zKoZVVWhJmAzA1EPzUDWWmwu1h1aa4o2Y6QLJuPR0KXfj6rU2qC0ROJam8QJH9igw5zjHLjIw+dJWthCkQ1XjDT+wIY//8Y8MuDgj8f091Kne6op9Jb8Lg0W06ovrvWeXbzmdUIIg1TYpDkLMmSrwxgtB1SU+SBFYWiLArcv3e9ZljMq5PdDjFgttuEuZSLGWAiJ7kqLSCkwKqCiWDkQpft/PnJZNn01pBXDI2XoT0qbS4y9PX5ShSiRPSsksg0+JvdUochC9KQhNKBS923X4tQB9Uji2ofZO6qvCcy/1v+9gJbP1D/X2PdIDYTd2+Y6vztlimAqC8nKJwU9A6Z0JDfzAnWPKWOkoVMwtUfnOmazGYtVhTRhG5S1BzCVJUyVaG0oqoxRUTAZi5y9quoBU0Qp3ttMJKxGm2HM9MFJhL3DsA09y9Bz+WoI4A7f8zgoGuHgoTPHQ38FNV973sc5LhL1m2c53qUIOfIWmCK97yQVNoaYjVCjgvFohNGCo5jlw6baFhoXhObKspxRWZD77DWYcmlER78+ZaN+NabS+3o1pkI4VE2R55voZ3UQU3N3YqEvAyIgMIm6jIQUpA3Y+hthal43YfibhhBlj1URocNjHAJx1N9QNvzxC47F1QUWFkZcv/OH1JNPYNLCzZTHKp82ElH6KKVEEaLne5BWgRdeeHYokEYFX/fvQmnFx5ae56Hdd7K2ssLy8hK/6DaYTqe4xrN27kmuKMVGsqnIizFd/TjGaPKiZPQjt0hqC8z+7AWaqsH/4bfpR6gePA7kscWUaPRHyqvY9gMUWBx+7vDP9POme5D7pCmvm3bYzEFs1cejMcEHuq7FWg7YwR+QRSML6f+x3yC3Odol7zBjpVifdP5VVXHRGGxmmJgxv5ptUE8ivzlbIMbIyokTfOdazb3f3KXrHLnRPPreBQJwLMl0862MtnOpH0WsF+o2Sid5jBCkmapt6mGjCEne2T9L14lNxgCFgXdVtG0jyhQ0jx3/Q7yvyfMxH9n5ebH7TtMZdRTeeM779w4EQWpX6cDTqd7SN5ZprVB9k2J6Q71temR+aKs+HkvPKrzN+lA+9+2z3Lx1hIWFMaPJfVx7ZRejAlu33kemPVaJu7BW8zqktRkv7M2G++7XHuLMC4Z+VOwV9ZssLj6AUiWEHyZXOWsrqywvL1Le8BTT2UzGMGdrmNVHeTZotNPoUvqctNHk2mBsP19czBKbqk5TIl+NqQjOE6/eA0C38vAwMbE7uIGhcKsPg+8xJddJ92kU4PBDLexF8xmUU1y3sI02irp7J2eriEqcjLWW2/NfYL/d5/nwr1BKcd3SjZRlyXd4Brv9ALEVH0A10YMwQGvN09eexRjLe6/kvHjyST4xu5naR3aObGNtwcSM0HqZleWGC5cqYggcOXGC/1JPafyP0DlHHg23rO8TgOe2FlEKmvwR2tZR7n+EznkMgdo+xGTAlOcM36Ju55g6Nb6N5/krYoTnHFw/vhVfrRCRSZMvJurp9MaHeKVo0Eb6jHxXES4ZTD7i+m7MTNfEdJCE9ceJLW+IKbv9gOxXa4++DqbS80zPwZiEqejRqd9FRRk/LT/WBw2vf73pgdIC2dYfUbgJ2arFZwaVTtSuaaiMUCOd9zSto0vRQ/CyCOQgi/xPq9cKt59blpYnvGNlmaIo+aG9ZT6ad1w8+xRKK77nHV2bpvKl2RcBafZSPjD5kdMpO9Fs/5tnhmljw8SxA/+bbtPABfaRkA9+YLCGn1aHf+/gpdL3UkxOKkGQZQadLDSKT52GzzwxdLi3bcPPTH6A1jY0bcNnqy8Pdt3DKGXvhz85jxDm76OPzpSSTvibb62Y51cSpQilpPlFtyXeT3f4tIF63sW+RLRW07YNn8lzfCfGdU0rMx2aDv7kpOEHv7tNL2nU2iZ3Ao8Y88n8ba0V912/hw8a7+Or3nfkf1w4zg9fbsi0Z6HoMErRBcf+9jYuuCEiMjYjdO6AsaXGWCM5P4aua1D05qCiZuqdULXq+XdR7nnvZOCT74jK8Pj1f0aMkXte+mGCl9Gu/m12oLQRvrtVseQU12rDJ67dR0XPtprwyETzkdlMDAd7TKUI9/obnsBu3p8ME2WzyqwhK3PW4idZObEsdLEtqGvJarVStBfeB51Dh1Tk1YbeY1F5n8YBaHyEpqpxTjabg3M1Dl4RsFv30eEHPPjLd+NjK7Tv6iMo4DTvkt+u38EZ9fTw+zfFdxHUN1AoXLhrCA5Ohtt5OX8mDYMjrUfJkr33XNfcwnT5LONRw2a7zCx/mBf1sxSqJPjltEbm9TK7dR82z7l8smLlAKZu9O+k044sy1iYLPTMDwDGiB+dsobHneKTN0S81/hgIUJRrKG14QNHJYDa238v07bimTKTZm3nuLa9g/HuhE0jmDrGDZwzzwyY6kJH4ypijNy89QDGFqDVgKkbr95PCIEuOk5UjhGKEYFp3rJz8nE6n3H80sdZ9B0vFLvfF6aaupKDp6peg6k8MwcwpWkWv0Kj93C+Jdv+ML0jCDEQnBuo+ze63vRAGY3H5L5Mm1e/nOTBdM5Lo5HWtC4QMeR5iVpYwE4m8gJpQE6RG8ajktFoxHhBbKSNMcmGIqamQ1LDU/9XlDDhfdSKovNBIqMIVd3IDezTwyQNVgcPhz67ToeKHAQZWklNw73KNXOQNvavkH6nT/VilP+jFOR5xjAEJx1YIS3+ajaT6YhFjrGaMPV0rkN3OlFgat4kSeJMfW/vIn+w/89YA+SU5XwEstBq87RTa4OMIlZEEeOk2oFE+VoZMmspyoJJOSYvMumC3bV0KQU3adKiMXIfjVaEMHe97Smu/kA9tNMk+/kQPM7BtHJYLQ65dT0DNacmjHEpQuq7mi0gdJ5zTmS/RFx04nGWNomBznROhmglpZvrWpzv0NrjXSNrs22Qet6ByOFtco3GY0JRyCAo9f8y96ZPdx33nd+n+yx3efYHILjiAR6ABBdwk0hJJCVLpEXttixLtkkvU5OUqyZxUlOVceJsfjN/gKeSysuUU5NkbEuUxyPb2iVqRNsjgiJFkSIJgguAB3jAHXj25d57ts6LX3efPvdeALLHMXlYfHCXc8/p093f376Ehg/xjzlM5WWFUYKpiYkWkYI4mxAhq9OhlUR0u4Kp6XyOTrvj2zkYxPeAwdbVcttKYagrPitswq/VGAsruXozpg1nrnEh+78uUSR4SyLBQllVNlQfJPoPb/Z2QpmLDlMKtFGemkeRJk1ji3eHKVvdtizEVK7FN9mNu2xtSfCK1pInUud5OEyJb9I9S+01VXVFZe/XsUmwNmABK6gpbYiUwtXDVTpykPIm91arYmKiTdqS1IgoklLwKnKYooEpbUJMcUlMEWBqt1ey06soqpx+bxc0dp9fHlOJ9V1Js8GLYyrLB01MlZm3QlRlRVE2TZTjjksylN8tN3i026XVitmZ+RxkF1BGwkUz2/cDVZEVoKIWna7moemPo9nyNk4q6MQtZmLDVEujioxyY42iLHn5pRepPnw1XHUlzjdS/OAsIIT62Ad+kY8887f+wXe+fVL8EmXRmH3XWVEpI2YRZ5P01i1ng46kKVUck+Vifw4naISJKBdD7niT8ffrtFPK/7gsjqyPH/CboywKdnZ3aLWkw1sctSiKUkpbG/FFSavSyN/1M8X7SYj5hnqqcQ+ltWxoJZuwLN2zFy4Ao3ZvGJHqIstYjbE+lVJCrL4Q3UvZKimLVaqqYifr8adpyhfXlplcnCRNEtu/RHtpqixLvjx9jQfdB85v4hoC+em3ANO222Bewvp23XY5G/SJk9h2jqwoVOYJRVWVUKV2/SSMsZW0MEZe93u7YtfVYvb5+mCD38pn6fV3KfOKLOuT5wPKMudni9/zpc2LgTT1UjpI3HyPHFPT0+TdmFYaY5AoQGWlwNWf9Olfr1Ba+nwo3aLT0ag0It24D2ZlrovJSTrtlD3mk0y1JlBtxbVT6+RFwcnzbfr9zBPGav5JT7QPcRTFrZzVLyGVvA251VZdiLsc1nlb2bBRJ5ApUEaxf3aTc+szHlPttgRk5HkGW79APvs4kYn8Hoi0hKajYFm9XG+d+GX2Fw9hgOXkFabbLSLrw2jFLwAWU2XJ7s42rZb07mglqbcEgOGaqTU2NsQ87Dh0WVVoq93NrT1FEicodRPP7J3wjOTO89sWU0K0IbBqoPjhWe1NtQr46P7C9lwvvSmy29FMTU0IpnZ6pGodMKRp4lMHjujbqVRFoUpKXXJE3yGYiiNKl1s1BlOu1mBelBxe3katfQiAjD5xHLP/rbu8FeP03I9qa02VNDClpo8JpvKC/u4OID7NOE5EG8ozev1d+tluA1Nq+jGptVZV5FlFaVt7XwpTl05snJtjIukSKUNpIM8Nmoo4go2+QWcGVESlEh7eXsHoGLV3jiRNyfLSEjQpYpaXFRtbO5x88QVc1JlSGv3oOW/I0Voz8dkbfHnvD3/7b8lyUSWLsvAgcTZ2J+lrJaGW2gLBOY2dv2JiosueX72N7sQEcSIZ5L1ej5WvPc/m5pYdi19Lv7FqdlIzE6cixx8/gMuXVMD09CQXXj9HUZRcu3iYrS3HrAQQm1tbKLUNahYdxVgZSfqH5LF9V/k7OlOSM5GVpfVfGNmApZcWnOOuXmSR1iJhsKqWMl1ymzHGSneaWMd0ux06nQ6uhWiY6/F7ZttqINDtdOgPCvKi8szXSX3tTofDN6wRacUTWxLlFcURT31wD8YYfm3lDKaqeGT2ulorqyoeWn+NtfPXooTbktPHOQolMAFPuH7l7VcYTF9BmqZk/Zwf3hQzGLQwJuZQpGjH4uA+UJ6nMM6BPd6U+W4ds/Nz6I9ERMrwkdUeg55UFrjtnR3aV7S5sCVO4lIl6HSGyXbMVBRz5OoOp853LaY+xYHWJm/sCKa2O48Rn78FgD3JNmfaL+GCPQ+b20iSlEhHnIlO2CQ9h6nSW1GVDao4zFG01pzRJ7he3yYY8FqGXW2luHMxYmpqaghTiq2tjK3NO+3Jkk9xI3f65w9X47R6gdejPxeBREmZptP6hL/X9PSk1IsrKp7ffZrKlTcxFYXHlGJVbVLt7WEK6UiYXH0AYqko8J+e+gQf+vSG3I8XmFstWShuEuGmLBlkN3sGc9L8X7zG60Rac3Bhm/W9H2T72dw+iuDMWBwZY4hji70rf4oyBr29jb5wNa9vzfHY9qLH1C8d+rbFVMXB8maLYcNyNM3mVo8reqXH1NLeJ6jKkmvf/ABvXvsM7XZMpGH+9G0YbK21VErVa5viYIzhyPbHPKbO7DkmprtxmIqdI180tcGgz6DfI01Ter1trhl8iUHR443N01w5H5Fc/58wVcXr5w3RykdwQSEXOy7TYMtGHESKid3v8ZneTSjbgTHOClAxWsdEcYKempIii1GEiiKe+8lPGdg8DCnDoj2hi2LlN2rnU4ekQBqyt/vfPyP3tFFPLvw0sYXYXCati+qSBLn6td+1znSkFTOfv1kKU0YRb3/1GXr9HoUtwuYzev1vQubh1OVmlJfBsPbXr4C16SsF7U8sklpCfOH7Z4l/cT/GQP/7Z3l44QHSVKrC/hfnz7O5s2Ud1YrF3TbfSH7qI8SKUj535d6dtu43tNeoVINJSA/ueqV9JJT9jdYaXbme0y5KxbbejcQkFjITiUmvX2MgSXLyQopCuox1l1Pzz3bfotvtSobyrhDzdrvNb5oN8R/NzmKM4V+YbdlXGClMNzvL3OwOxmAZZuVNnJUpvWQrz9qmff5FWptt4qLin/d3bX6M4RuFZrcnLRS6k1PY9qG1CvdeOYzhrpUd0SSTCbSxpWqU5sf7prhz5SaJtooTbkoeRMcx7+y+xloRAZsUec61U+tUVSRapKpQXXGoO0wdie70JmBjTZV5ntPPej4icRRTcL261c/9EX07LnLOCQ/O6Vua2+l036DdEUytra7S6/dt0EZR70MV2PTscUod51B1iwgVQ/ja2XXOf2sSU5okSYjj0RQAHdVFVfsDyyAt42u1WmAk0qksyxFMuaOsbkPrqrFHJGFZ29ybGJt6A0AU1Waq0Fx2KP8dluMv+9B4rTWxkurqVWW4Mb6j/k3kmKLh8L4+L/QjoqwUTM1t4kPiMdy08zEmTJsoUrwTnbeYajE1NelD9n1gjKnnZn121lt1kjT2gqfMTVKbIJVi+dydzM29QpH1MVVOp9OilUZsM81W94ewBRhFd2qK6Xz6spi6dLXhJJbqmlqcPElnxmsSYocVyVhKWEu0SE303P9OY7B2RMsh3ebXNjwW8PVpSudgM3iNw6uedtOEyYk1IfUcwRPaNEnpdDqgFYPBgM2tTQb9gS9OR2Bmd5vb/3WLFXwnJk9DYQLpDugY45lbFFlNAIn4ardb1q4JnXZLqgbYyqRxFPnoJaUkAkrryGa015vchSWq4c5+Ssw6UTCPzt8h+6zedNqaswQcTUklimPp3uaf2RL8YKPGcUQSaYpYiZZppSClsCp+KvZiJfkfaZJYKc0xJjwzwY6tMsbWJKqDFpzZ0bNOu9TalmuPbKfQTrsFiMms2IjJBlL7bGLPlJUi8Ym475UjjmPiqJA9kth2104La08QtaZQWsJ6lY6CvB7jbZxOS6gxFRNRM5Q6jFTZkkliKZA9awIcNf/XBHsosMC4dVN2rySRaLQoxSAbsLm1xaA/8MJfTW9qm6z7W1lTrAFK6nIygims7VbuHUWWodnwZdeFUhsj5mRL7TutFkUn94wjjiPvOFZKj8eUxQ5KSyUAsGHoggkdaRu+76mD1+7dmN26VEYio1w1YK2VhF5bTLnfGWNsaaD6/wamdI2nEFNxgKkkSeh2Op65urF42mQMExMTTUy5oCXLZGqmrZjodmilCZGWRO1uu4UhZcpMcr6MyDLZR1fMTjKZT14WU5dkKHNzs/yWuV9O1Jq4K4Qr1lqCsHyIYc6JZ38mjZRsNFP64EFSFMUPzkkbU2uawiWnYUg+foDB98/40ESX1ayUInbx8dT/+BfGOgZVM/48TClptRMmJia55uG7aXfavPGVp1ldlR7kjji7jd8gdPYDp5F4jWAMV3bSjkKx9c3T9UJpKL63JJrLgwdYUTkGcXD1TrxO9MB+dGW44pQA4nPl+6msNJHnuWwere31FGjQVhtQuA6aMXEkuQmuhlhDzSLcuO4R6ueJrUTrzElxFEk2baClOAIg5a0r3kojrqJFkkQMBhlFWVEYQ6Ur6QWT1HZzpaVJWZIkcn/jkdycS/veSXxub4QbV2khEmmS2DydSEwhee4JSLzdpmeEoMzvmff+qvw91gJ4bm6WV2fluT60PiCJp4i05tkrJpjTsLxnFQPc+tYBNrd2KEvDlHqdk+YFYnMvrbRFt9NFaWjbysDsfox0ZhPAExUXrXWyeh4QTN1gNZAldRyU4pCxkVhW9julnmd/8XBjvLF+3r9utTtMTEyyd+8q7c4sFy6sWExtcUafQCnFwfKonLw6jduDp/ccs5iS9y+Zn9GQ5ILDhRErlLTE8JhSvuqIRklBS9udcHKyTaeTWiIrkxCtfQhVGdrttuwBpdjYI5npq15IWvI5LtvPZszMxlYYFHzt23yGxYO3opThtDpOXN3i59cRcIDl5GukiND40/ku6dUl0XKNqbPxyx5TB8qbLbGvOFm9gNo74AJ3U7Q0+1o7HCmPUpiS3GJK710Utrt7ArU1TxRH/Oxmg6nw5rjJO1PcZO47nTBhg6KMMRw4/yGY37AYq+dXW9zszre5e/bDRJHQnjxfQmtFls6xeeZ+8p0dlFbsu+ow64efoCxL9r9dN4IbPi7JUKSPuxCsdprQareoKkOR5yy//ApZLqHCeVaQPHiAREmP5ezRs1R/8yYA7U8fJnv0rGxyY6jKrO4N/tcvOY9BcFchilXwrvadiEoQCkBu4kI5KI5ia+bqsP71l9jZ2WZ7e4dev2c3XBX8PpCYG58NH0OZLaqu6jpsM6uqOhIm+9aSVaSsZqEU1WOvoZRi6xMHKX5wmuTBA4Ai/8EZVt98Ha01Czfc6E2FtcTppHq7KSLtpSLjNC6cvVtay6pANfcagZEImM+W74c40CXdHFgNQWnQxkqJMSjdYtUmWFYWFC7xbGZyQvpc286dkZLkOqkuHc6mU78tpbCmDCkV00zkS9OUVisR5pnEgCHPCvIsl3ybNA0S/myXPVPR7w8882m3WyMr+W4eRZ7xoc2MOIpotRKOXzMrhLAQLDlM/TB6ljuK60FpzsSvEJOyeEUfmTSBbT77YzEdlhW9/k2YquLs+jQLM5vCNIaO0JQrDOT4qI/JLlVlz8kqYRBRHNPpniNJU3Z7fc5fuMD29jb9fo9THOeQZSSlsZ1Hbal2wGsL4wwlTUwpNNq/Do+qCkrumKrxtRDH2FsztNIsXtUHoH3do/R2YwY6Yivd4oqtZ33y7dr8B7jzwvUYYzh73ZfZ3ob1PR8kjiPgAitzd7O42sPaCGyin+Dnqbk2cytPei3AmDrBGeCXD32HiYkJ/vLVTwaatmgvp9QJjDbEOkLpFovXSORqWc17U/+ZeIKZ7gSq0/HdcKUeW+Ixdc2NwuzPnriNqfe1xYXQTSg6ba+ZvN1O2J8mHlOtNEXH7yeOY168+gzX327ovHGQLMuBp6yVQaO7T8PZqynnHgcM/f51pCsflI6Uk//ABluSayG1swZZ7oFfFtLvoyitXROrOiISUqiOFUUhJdOxhN+qYj6kM9A+am078AX4F02mgVGe2wwrD2VVsWtLQRdra/T7fdsStPJEzPj71GascTu+3gzhb/ASF2AjywKm5A8VqE3KC4MetMbgS5pZswOmtDWocowpccTXTZfsnJ6nAAAgAElEQVST5ouiIE1bUkLDRp24hEllmvOGm/vg99KnvXJP4Mfno9qUOFSNMdJ8qLTj0MH13P8Y0lZKHCU4G7xWovYncYyz2YsfRPlhCXhKy/istmTNKnES0261iWLbRMhYZpKLH8fYuTPGBS1IXoTzr7mkUB29t6K8kljMWEohjZl6fcuUhZmWQ5gCfDh5ZU1Fbk0LVffXcD3VrcTk7+eDMoIxeBwN71dTC0KhUglAWdHr9clzSVLt93u2dXUlHRqrUASsNQWH+6Hb+LE1SIDHOD6yLMSZF9pCbqLsXNXGBf/XBxO4DpGVNCHD+oZ6/R79fh9jDINoQFEUtFopcRx7glxZM1WlKlvhIRhroPGHgSzGPZSDVSDoaSVN70JMxJUtT0TtD+l2O7RaKSZJcH5ererKIqYyUvxRKzrdDu22WAKSOKLS2j9/lMS0LJ1w5ndD289hZjFVWEyB+DKzSkKRTWy8GdBhKlL/wCivJIm9NHry+It+6Y0Rm2/rkweJUMTA4Ptn6h4UVeWr2ubfPmWXuN5UnjA7ntCQrP1O8WePaiT2TbBPw3OqqmT1a8cb9xzHCGQrKk8Um7t76DCOIZjgOu7ihqAUMV4Ct/dVKhxfXZ6i950zkovzvTNeekk+sYBCsfyDl4itSbBm0vXzX3XgoPfNSIc35auDyjnGaxrufWi+8/ZVZ7ZT9Vg9Y1H4DV56xlYDyC2TVkoiiZyN2UaXOdOBDwqwdmmMsaGaUmAQhUTPKHEUp2lMmqZSu8nmCw36Gb1ez/rXpOBmnUPgEjFtUlcU2w6doV/pvXEkSUykhRB/55USXl7zQobWUpZdKcXEHQnn4kesj0n2TFEWDRPmAkewP8Vt3oVZadt6mKOAYkm9aE1bAIrTVnMxwP7iIT+u5egrLBQPU6rAPm5gOX6k5izbTYK9UP4mCjjo8ANi6lKK03M/wgCLK/f6vS/tfmFhVsxzS0ivkFGhETHJVbf4exXV7X5Y56Kv4EJqQbFY3cJp84IfdKU1pbKl5ZWRRm4GZleeIDeO7xqOvr7Cy/yd/CoTIe2a8gWiKKbQE0yf/zGluhUFLHAzBRIOf25jmmq2aGBqf/EQE/kar638b7S7HbhyH0opfuXwdxFrgWBsmRNEaKgMpZJQ7tP6OBjDormF5fVpUNBuKc5d+RMWudHf4+zeY2zOzfLhzQc5bZ5H21y4I0deYXH3Fqqy5JW9T6NzBc7vtvcpXk9i0iThcPQ7JGmNqZteWyDPnuLl6jEvrBibs3KwvInTxRbR7j3SmfOKmMW9PSn9vz57kd19GYby7JM/kbXFOv7i2MkfkoT1zVe9phHKEP64BJjdZvAS10Wkmfoyyr9RgdlryNrUYCL2A/95yAzcj51d130Q+mGaDG9Ic1LG902pxyHUWCk1NA/UjMwMG/msVGqlqN2/etU/c/TNk7gy5O1PHiR/dBkDpB9fYK2q+NKp+zDG0NvdBVdHS2uSOPEJoXKtpt/C9c/4a35CV7X4XfPZxrw3mKCVWt33XqoMJN86ys4+kREHrms14Jr+mMqQl5l/VmE+kkCmvEYrjGZ3t9801QU1okIJt36e+jD2OlFlRr57t48LK+usWDPKh6+JbRtZ+MlcW6R9RHs8f6zH0YXrAVhSL7JobqGiGmGQo7J/3TtDzMwleZBBnivxKSll20zr570GXAUXPBd/BQwsrtwHRvwgh1bu9XdZmn+cSD2H00a8fGHfOEHl9PzjHlPF7DEOcdTenxpfbvRK/CTucJhSKFrRcZ9xH7nWCJZmnFUniIgac1GWhsJU9LI82C+SZCiY0pyJXiSm3udKRVJtHOjt9kANeF7/GO3rBtrBTUFZfqARMfZa8lUuxKtcmLmLa7MXrDWm3rNOG3H0srJMbUkdt/KoYkmd4NDcUVDw2uYMycZ9MGPnxhhp82F9PNrUyZl5npPZJMQqECQd7gVTJevFFlVVsay/jDGG6/KHRLgJfNFuf53bmGX/7OucfCf1a14WBUZF6PwfmClvbCSPASlyVjWlFzeIcUxg5FpD/46+oWFykffBO0fhDHWxMmiou+PvOOZrNfrWMKRJQ21CGhqbIfCfNK6pPAj8nd2YPfACwugVhjoKKtRCqqoOm82+ccpLOfl3TqOU4vwnDgDUjObBBTCQPbqMLVLCdYeub5jDXDgpwJfS+3g0eQ6oM9lDzaoxe6r5MtRitAcLVoMwAROqNRovMNjJVnYeja837c6tpU/PzHU9nZJNbzVcpVC2T70bX9ixsCkhvPuHqwwO8OgSPHBACieuPVXXiXNC1g/OuDU4SnngORwYz569nfsX6mCDYUyF7yttOO1Ln9TEBhTn4kcaJy/rL/vXixfuw41GAYdW7m3gfNEyF3cMi44hpg6t3Oc/VPObfo85c7p7Bt0YuY3CcqNVyjIjw2mONwTQcFzu9SlecNCr59W4jqKArUOINYsd5ihL+kURhMAKrXKFqqwY2PYN7mj3/o6eEIk6wx64uvc8SSsVplGJb6QWfMKZCV5bLB3iFp7e0wXgqq0aU1VZccocp1u1qYwECFA1n/+0Om4Ro2xdOyew1RaL5egrYrK2Qu25pF5/Z6nBCpMH0Lxc3MbVk33iRHL8lten0UpzYDzBBS7HUII/pjRUYyT1cBHroyYExtSb7fLQHjZ7DQ/GvmiApn5Vy8zhBhsZ0uilQ44eXjPQUFRwgToDf+jzUIMJBulMao6h1PMVPMvQ5DgC6UhtpYwl3njzmANHHXViHe5WIhUCX3gpTwiwc3pCu92ysfz4aBcPcPcogf1W/CdmSEOpw04dekNGMvxQys233ddKAZX253sxwc2xo0rufGPs743/XMDgH2vonqPDeDcPA9ZvZmzekZhRijLUvIf3uO3EaJlj5fOJLn4P/9qYgJD5RR1zomdl7ofBV4rLTuQYjuIwJXtFfu8gpgJsgcNUjWDxBYZj9oMUvuqFNPd/8EAeU00G5bWDEUwpSiqvAdaYbuIWt8+cFuAZUj3GdqdNGsegbFUMjznVGI5SrrqEDvZ2mBpRZ+ibcZhqCHjOauCM+OGUhR4oVf+2nlF/j6b2K5Uvhk53l7zocUmGUl4k3ri5xM3bKbsQNUVqbsVG0tOYozEloU117KuhsdiN25gW1ZSKm+MMrxFujLpRk0EcxibwdDcd18pumlrSCjW5MLIq3PzjtbpQIsMSc+OmQpiFF7kqsm+Kf0oqBUDx3SW0UnQ+eQDn0H3r+6dJHlwQH0xl6H1viZAW3aeugRuk5akkpTpHJh4sApzx81/PgztfeyIvpjApzxNOuiuJ7S5pbHXqyj6bx0poqrND8LzcU6R6nmWZVc3MLyZBvIuHi3AE+IXrKlzFE1fEUjSPoYErWDL1/vvo/qIO5gDP9BsE2R6HzFG/v5fU8RFG5fwoy/Ejwe2cdOv2umletCEBjRcCnQ/njDpRt+CeW/cXcG2a3fqGGHSMSKuIJSt5Hyxv8rduYgrGMpWRmbAY9c8kWtap+R9RGsMr5gUILDlaufq0LsBE+fnFatBVVWFKQ2E1cq0VM9NTnoHkRe7zvepWCgGmhqZ0mZe5Qgp3UO2BMo45Z56gnwwEFyrwk6paCEPB2tzdGAPza0/hmKfzL7rfDpv8D5mjGETjGx5PObPAG6ceY2/7KgyKq6cWeGf3dZnS+Y3RBbfHpRnKUH0ftxiO2NlPxxzqostaC+dm7EasTPDrYPH9tQIzWK1KhqYx+eOkMhfRFH7mT3aEPRCVxhFLe8V6QzqJGRfZAT7D1bionIAlXUoS/DmPxmw3CKn0pS49lzD0v37Sbz6tFcV3l2h/8iA61rRaif9d8nFhNK9//1UfmReayFw5c5/4NsSFG4EVOKDbkGat0UpqIRkbvuylIceflAt3litoJfWHTHC90FxgjKoTw9y1gnOdeSayAQAuPPS9dEjmtuCqqG4niZ4HFB/dn+P8U+5wob9KKeK12rm9TO3YbtBQu5eX1HEWzVF7jRdlHoHFlftY2nOMQ6u1uUpxFlAcVvf6T4ReNRl2KGyBq4RdC4dNjBtO6xPuapyef7wxBw3/WyjlG1g0N2MMnFbHKaqCDGn69rJ5roEdpzEALFrn/Qh2g7/h4c+c2xgvtRszhCkx+bkoRa0VZ/YeE19MrImtaS709xkjHQ6LvOAacwsKxZnoBK2tj3hMlXNPsL7nA8yv/aQx91hhLF67h+umN+lXfZ7nRZRyKQQaQ4nzySjw14jX7yWfPeYFDJd8GQbT+PnXmiV1HG3knP3FQ5yLH/HnXjW5QF5kKPBBNVproo25kTl1x+VNXuHiWMnSVxJlaBkttXCfBXTbft/UIGpzVMAk/Eeq8W+DmQWrr4Z+P+4JmmAIBxRybOM1nPDnNZk2tZnFfhFGh9UbnSHNKlQRh7d8LeX55xl5kJFZtG8t8VHO7OXGpXCjqTBSNNMYilzU76Ko/TISfYUPCzTG2Mqockjl2brhGAFx9/1NtMJoDbb5UlnaPgv2wf3vVf2ZW7N6WsI9NLS2wSnOqtYQIIzBlbhVdoyeyL4HtZSRHWAQ6dWEzMSLIo6TBkm0+LVz8xiisBEybq8SiERCxAOtYsRn5v9RF5m7IUwFmrnHkXswCA0V/ue1qOUEC+MeiqKy4dOUlKYMmm4R/MpdPkRnc3xOLB0jQxJSr4Zk7nDrTU3hmO2/NtqyLA1VaTyTqdfCmbIUWsu5UeXm2DQwVWQZg0HGIMu8wKq084EoCit8FEXpK5uLs7+2gIyEhV9GgFJD+BJM2X2hJY9QojP8Tqmv6TB1ieMytbzqO4emqNA/0BjdEDNpnKKUj+Rp2puazMBzaBVw6+DfsaxDBfdzPGLsg5t6Y4w5qTFmf+KQ5OOvPxqV5pmd51OX00SasxSazrCaTb2W1u/A6Jjr+VQenLU5QMZYfOc0UDvQlYbqe0t0PrVI+5MHUUqc+W+fW8JUhthqL4PvnQnMSXhpcuH6I1b6CfOPJBwxThLJ/1D4cv0X3ejD8+ueKXwuOxHDFmKl8IVG7eDqZ3f/XW4J/qkPUzPHcHAOUyHhcyYhg5QbKeeOAYqF8iEwz4sA1GAs8jsfSWWvFQpnrgOgex/+dhizFxFlYG7DZsLXzzAsNEU2RLice6KBqUVzi2hNjQlxlzK8ynP+WqWpqHQV/NY65YP+KvK+qZEt2jBpBSzpF22VZdF6jDF+fhw1cWNZNEc5rY4Hc2K/VXBm7xM+BNoYWHjnHpxGeGbPE16rk1JGrtagmJ1f42Vpm2wUxeyPPFMyhSG3JeVlbSKur25Fac3fnU35xYPr5LnUIovT2HeJLMtS2gCMgVQxeyycUn8MM07hJPKMhzgqwqZSHKx+k6oy5BSNi1QNTF0cVD+XhuJt3Z5ZKC89+U0nonkt2QwJ36HztsEYVMg8xt08nIf6YWqfxfDsXZpDG/B2xaaUcwlm2XyQgLFe+hhhquF3DQ0seH57e7dmI87Xiwyvnr+mGSm8h7+EMVSlSFn5N055RqZdeQsbqhwnGlp1ElP64KLUbNOalR+8JloKivTBA0y9mEvBzaLgd6/4DGkrpdVKyfMc1wVwmHmME1BMwMRHCKJpElnHxPK8kEx7I8lnZVWRKOvkfo8xFC/0Gvi7117k/oVw/5lwIYlWpVy5c2q7PA6mDEV5K0l83JuMtBNmcET0VpSCI9T5G2bPJodW72uMx5lnRR6sGfjIOimEkdiHGBayGq1459YZOKLv6JJdiFd5zl/VMQGl8CHBw8u1GJaHsYfLqxHGolg0t3gmcJhbbdqHjPkwR0eEzLByw8r8XWDgrtWez9Fx911Szdy7pT1P2BHKZ4sr91q+Y/shlRULxVEJBbY0zYX9S+MwJa3Pg+O6/vMc7tyF0hGn9QneUEsoFIevV7zU26XIc4qy5MorrxBMpSl5UTRMucZAvH6PXytPnQyUc8c8zVyZvQul6pIth/YXMl+WssfR83Z+SvL8RipTcTb6MmmUEOuDTKr7L4upy1YbBmxO3xChdoxAnqJuzwoh7fIWIrlcEHESAGAsETfh0jmnVvi1+1Y1OabnpOF1hn7bUKHdc5rxHGCMOe1iTl+5TO0DaDDPBtOsJUJnyvCMIxhXPV/Dz33pQ7sS08qpssrPQ2jKcI/rwOj/VYrih28QxRE6Sm2Tn4LosTfQOsYoTVlUlIWU0dn62gucLwtfScEYY/0YUs8ojiMWrj/ik/ZqLUmh/FjtPBnrCTESTDw6wcEMKlcZVvv5k2KlsSe0lxYv3oXDCkVKwf0LzXh+NYQpz4j9b/2J9VtjpGqufd4keoF4/V6Y3awJaPj72XVYm8HnRBBsY+uXcGERTt7yUqmrtWaAoGufmd+AlWnLTDbAwIHqZj/UBgFam0HNb45Oi73wYqhNAEs0Awmc8HPIHLVaAMSmrvu3pI5zsLrFaiPBnNuXi9UtDTPcXas9//2iTwC15wbvFYoz+kVPug6tfRgSuXYSR56etUxCqhKPM/+v1Q6jOGJl5n1UVcmd72zxevs0y/pVlNKUWUVZ5LYpWlU3zbLCb5FJP5Y8yyVxN/Bvkksdu3L+xyQb91HOPWHnyzFEw12rPX4y1/bBVsYYfrjcZHAPLJS+nFFVVRAJPWlv/QIqVp4+XOy4NEMZPgLG4mVeP2mW+dSnNn7njtqZFhBLA4FnpXFL7xuguTOb2gWY4I/7rgGk4KVqnB+MeYRWj5m4y9Dz0edvErYAqg3Ajj5TLRmNn5nRIzQXCoGRz7TSzasGRCvSUR2FQ2OKUWibUS8tSn0VaCqRnGwLZhe84RmtcZn9he3nIsUknX3YmQeULRMeMphQUxle48Dp1pBJnATvQRtJB8tLmtreI4dfs8aHzc+G+EjzNARTlVK+2yWWAYglYVQIUTRx2MDEyN6kFiyHohf9EQpDF5cBhkY9fM4QlsPv7d+muXNYWKjH7c2zfg+NveAlj1D2c9YV3z1WSSVkd53ImncVipiIVpT6wrn187nLiX+kiSmFYKqwmCoDTNUP5CtD2FJDzkGvtcIMBtIWOxtAnlPZ9hiqSWQdPO24muarmkapGlOMYmpE0AmOyzMUFd6AAMABARk6v/Gve2tB4lrFOvDXIAl9BuGP3aYIpCN3b3nROJqE8yIfXkRyHU96xm1b+6lpnhY6O4dtWJ5Ajpmyi97ZLuo4UNQbXnmpTbnf+Pfy2rVdDdfOXb/T6dL51GEpAWOg953TlHZDl4U0N5IyKRV5vuv9KSq4Vv2oIlh4pdvIpi3LnJMvvgCGWjJ2wIxFi3FtEBZuOGKZjmpMi3NGhhpVmFCmXE6BlmKmWtVJc++1I2T87v2w72xJHYfZxg9InMnLX0j2nDGGyDwrc1tprpvesDTIwOqMvUSwJp7cVo3N6E1Ww4f7cGU6gFSg3azO1OvlynK4TTu8BPMbjR7zzuzl17VBfENMNffDaV6wDMxw0jwfjoZT9rtxx5K2Ppah75sMfRRTStl+MThTVk1UpU23HM/MT3Jtvyv9iSymyqK0vVkKyqJg+vyTFlM3sG/rIMZqdwpY2nvMDkFGJL1jTG0StEwgjMBVQLS9I+PevpUs2iLevU38mHuf4nolfpn/eE4xNW94YKH0zH/i9sgLrXevDTChH1xJxQsdNTF1Zu7Y+MnlcgzFax9Dnw8R0jEvRz4RgSiQiozUzhlRoUbUijESzZC0UnOPUMpRnoCNXMtLZ+PuEDCCMdJtzS+GvhuRzGoGaEzVYABe+hy5fOAjCm7oebllEp4ABcw+iiMi25NGSttLf+7WJxeJtJYqtnnG5tdtKW1LuLa3t9n+i595CchVTA3/x454WIMYZXb1Gx1Zk5ctTtf9zGHiOCFJEsqipChydr59ksFAivLleU5/MODFZ59F4SLPIg7ccMT2thEm4yQ2F5pZ5CX9vrSCdWLOoJ+RxLGUqbiENPWuHJ6ZND8ONVGUENol5yBu/L7eEHl5GwpIoufFn2QM1TCmStcSwP41jN3XgTRXD3MIUy4SbRymml4vhvZ2jakl/WLjM0co3b4+Q/1+yYTO91orcoJKOF8Xx1T9u0PmaM3M7Hk/3dO1TAPuXus3mH0cR77XUBzHnI1f8kENet875LnsW3rGM+OiKNne3vFzOh5T4Fio0grlCpga431cp+YeD+ZVDsGU9piKk8Q2AUtQk9dR2PF4TGU5b6Y3s0yJomRns4faavOtl0pfpumBA7t+jnI33qJke2dbpknJuEJMXeq4JEMZ9ZsEK6GC70M6fREC497W2q2dqspInkIt2g/9zjRG4sjb0PYd+m3IyAICbUHTOGPMLvRguoy5RNFc8BFV3QtpzXnyvxphnu61XNn7HLRrsmSDCewZbqNPfu4GtI7IHj3jRybZ1JB/61UwhsL2zg7rYxk3P1ZqrSrTyANxDzGqFDefzx3dz1xPHMdSeThJiCMpOy9NsVwpfqmOXFWG7q/fSZ7nlEVJVUoHzSLPKcuS/vekV8WZV18J5md4F8hzXrlwkIcnHgBlfB9v1+/lUvbed+MYxpQawpTffcpGa1lMnXaRUcbwxuYZitljXrp3JaO85mE1CRE67G4Z8jM2JH5j7zsWVAGmbEKbWZ1hWTuTdf0jN9Wdx5/kis/eNPrw1ndz6fkZRkNzk40oPgr2Fw9L/sRY9b95tUPqKGf0CbRWHFa3cc9mVpt9lPKYarVaPk/DHxWUVUFh7zOKKZhdedI3CjPgC+WaYCySN2Pfr84Go6uPQ6v3YuY2pMlhHHNH6z6SJPGVuIcx9dRtKVWVsueU5uruCmWhKcuSo6WmyAuqsuDJD05SliWd2yJ7t4r+6sDP85J6UTBlKvLkWba5mcnJSdI0ZjD9I0hTabjIP7DasFsw929IGEeYzQjvMCOfN4ioBY9I7zYk1JpmLhVFNXa/D30wvCkbn4SgUc0NLENSwevgBqH/xl5kPD8YM+rhMQZjaJqgAGundGGE0t5XbJhV6brvSWXdKIpsB7eu0J0kEaddIf3n5fySqqy8s9xrHVbl9eW2gab0FA7en1A/rqm/c5pDHMe02m0mJidptVpEsYDBtUatL1GX7i6r0lbTFZNAvz8gzzL0xASDwUAYTllaJlTXu9K29w4GlNZMTk3K57YrpakqjO3a+F47ao0TmsT4Ugy7ae92Unp4Xm1WttqGCYpjupv+HKAaK6gNn6OGUTb83Zj3I9aIWnhyVzKN74Y/bFy0+d6reGoIUyIYaqWIkb3YiqUydkd3rOO7tO2na0x1Oh0MkJjEmqdKylKc4oKpcqQCBhY/YTHVGlONwQfPXNMlL/p6hi/Jxa12m+5lMNVqSz+UqemUmamqgamBxVR3IhrBVF7kvhRQoQvx/RhQWjE5NUkxOYmO1JAgenFQXVpD0XUQmp+HkNi7i5tg/UNiGfwwVEC85KgAFfkBhm6H5u8vdyg8cavvOHQR1Tzb/6l/PXIYGLYz15e6iMOPi8Ks/lbhIyna7Y6Uh46lve3EZ28QKcBuHGfmqoyxDm5Ra1e/9gKDbECv36f3lacb0plfE8C4XhANdbupmYw+c/0Usk4S8y7h1u67UQ/F9rdeZVdrtuKEOEmkFH0rZfqXb5be4K7VsJ133+YAYQqxSpmabqGU4op/vs8nSpZlQZbn9Hq7bH/zFbIs82p9VVacefmlhl8njjUHj9xkmfF7qx+KCvITYJSRuPB7S5/8dJe6QlV1IEu0dg/n5CsWZjc5t1FL//ujWiMZgtTfA1Q1ptxxbkP8MQuemTSFIfcsvfs+yLmNUZq/f25DSs3bEjCNkXm5bTx6hgOZw+Nc/AhQt7Nut9tCdGPpx5OktrVCvIuJYw6qg4Cib9Y4UNzkMbW9tc0gG9Dv9+n3ehgMq6zUo7R4OXvmNvbvf7aBqbNnbwcDCws/G6skufGfPXs7HHwelOR/nJw/JoU2jZHot7WZxlPmWWbbNwysiSshbUlb8zjA1Pz60wBk0wZT3szT8x0/ZlcN/BqlRjB1bt8u2WBAlmWs/LhjGWfF4lQbgGTjPhH8NjVX6uSymLokQ5Hig6H8bhq7ZGSzUhPq0SgML4s194MCodmmec64w0tZY9Sexk/qe4zwE09og8z30KwTMJnxKnR9jab+XTvwtI5s8phEcUj5dneqsgxFzut2O0x+9oj0vDaG/ndPY2wnxLAXNEq60qVJQpKmXPvbHyTPcrI8Y+sbL7O7u0tRiNPP12CzwyuD8EOMaUR1Oa1M2fHEtilVHMfC3OIEbSPB8jxn51uvkudC0F1jJ9dDASXlKqqyIs8z+kqhdzSbf/JjvxFjC/A4jul8+jBpktp2x3bGra+xsr4mlPSSb9m2x08++CWKsuBjs6fkWcuSPMvZ/tYr8vw2mOD0SyfG76F3+XAtsgEeW465f0GicR5bjkbCiCUvovaZVXt+3BCL9pdSh+uMfgTm68+XwJouDYdXP8zSHnGiLg7loMjZTkAMNXD5q4bOe+eQVEZeWO2xsDbDOT2qMbmuqvvtFjynDQuzW/4cqZTbvJNI5UN4C/DiMPXauTswwH1X30hVVZaR1HtYMKXpdLvSedBiqrIaRq/XryPV7JzujXZIk5R2p0On2/WYOjF4WkrYF8o61Us/5GuvfYaTJ2+1UnvFxyYn2N+WcZ6uqiFMaV5/7f1EUcShw8fZs3cvV1x5JVpH7Oo1rsj3scUseZ7x79JDmCvEHPz758/yR9zC/3BBuru+tOcYqrAm8F1NtBlzvb6NKIo4l7xiMSXM5cf7EnRZ/dyYKruSPDn32T28750tCoupU+dbFHmNqUGeQ5aP7KHwuHRiY2Cv9yvXcIYNMRdHjANmopTbmpexPVjGIGXJx0sjohiMuP/cYO2+vEhsjxk6N/wotBmYJrCafEp5U40oWDYO3DnqbIMkVxCuqiryLGPQ7zcUXBeZY4wk5Q2yDK0icTKP9EBXwW8IWo66b0XiTZIUrSPKKCIvCqqitKVXbOMc/0zGSy2RjmyP9tg3v4rj2HXo4MkAACAASURBVBP+7uRUI969KArU1CR5npMkqUSt2M1WlrbTpCOYVU0ZlMr9HEVR7G3B5dYWadrynRWVltbBPrw4iAJUlqEmaUJURXTaHZmvsqRIC6qJCQF+WXegk+J94wucvlvHcF2l+jNHkBt6fS2sqJr4C6bGKt9DNwtMLha7Iz9QASFXwz+vy7b8HE9mn4WRfxumusY1lSfsdQ6OCjClG5iamJyiqirSVotBv9+4swruVeRC9BymwjkNdiU0MGXqCgIeU9LAqoysaaiQisTiEwlQZYzP1YsiTWz3t7ZC1OTUFFEcMTE1xcT2FBOTk94/WhQF7E6R5xmT3Um/h1vtdqNpnWAqLLmS01O7KK3YjrcspsRnud3d/nthykVGgqI7IVGdeVrQ3U1lPEVBXuS0M2s+v0jRYPg5S6/4brt+susBua3hvvWqq/0bRjQ0l3TM7Uz9/UgUvhpKYBwdplWgaoe3smOvTUFDvwo+cMxq5Jqq3uBKS9RQFJik4kgkebegzlEmzCJnd3eHN778E0xVDtnARXtZXVlh9U9WrISl2fvF22h3u3TabUks1BL/bQw29yMny3NWv/w0eV54wMx+/mZb7kSx9c2X6fd65FlmM9WV1YDwmzOOItJWi71fuoO0VUeMRDpiHAExQBQntL94ux9/kWfkRUE2GLDx1ydsr/ecPMtGNp2pKopKWkL3LTFY//JPhMHEMXGSkLZaTH3uiDVXxMRRVMfzW0Hio9OncPtKKU0Sa2k1/IVbG3Nb5JltLHRpieqf/LCmrGGNOiSq7hCnvGJJuy6L44n2YmU7/QGFTWhz1z09f8xv5mVt2F8pllyxRqXqLophtl9oAFAi0hgDd72a2HEnGAzX2fbxzG/Y+9c/XLYmsP2Vkv4l4bXtd8pqvpG2fsI45kh0J1EU8VrrVR/YoaOIg+a32Hfln5HnOdOb7+eZ8t+JZWMEUyWrK6vu8dFaMz09TbvbpW0xFekILKZaWYeiyNne3iEbZBR5jjGGq9Vh3p5Y4np9OyjFYNCnZzFVVDlntOb+6Sl/36qqiOKIVrvN9MyMx9TmFfdw6FbB1Ozam8zMnmJt7iONad43kIKt/9KUdt8W7Ozbw2R7ivZOlyLPuV7d5jElFQIck4FrisMsvzXtL3j2mWUbURmTttq2rXHCzN2dBqbmVp8CYHXubr84Z/QJjIaD+mZuvrbynTKNMZxbPUlRFOxrX8vFDnWpuiy///AXG7akkcqtlisP6xNj5CD7QYPb2JdjzEqeL8nOa0pi4cZ3zGPoAuGNR5jJaIKUCV4PXyaKIlqtNlc9dBedThtQPhSwsg7vsizZ+c5JL6n71raIacsRSK01pqrY2dnxEnRDejIQxxFJkpKmLSoLkMpGk5RlYc1hdZSW0xiVdeDFcUy722Hml25CKYn0ePurP5X+39ax5p5VaeXvlaQpU790I61Wu7aTjhVMhzl0HS3mNJQsG5Bl4gjc/tarDAYDSuvvqHyPhWG7Z70GTjqNE9Fm4jim++nrRZtxveKtf0/Cn+X/KqC04b74bz7+3/48IvY/yfE///G/8pjaejarc4SQ0vVL+kUU+LpSbqaaZigxd52LHqkxMSwMBcKZ/KSe8Xj9Pg/FJgPCm57itXvYP7PZCLMNOzb6MiyIGeuQOerfK6U8g1NKUQR5C8n6vZg9T9FqtZnfs4d2gKlr899A8yxVWXGyeo79+RFeLZ+jLAsW3vkglQ2/d5L18pVPWkyZAFNlw0dljPGhtYIprHM9xJQJMCX06JC5xd9HMCUMyWFqbXWFV4qf+SZadorF57Dvp6RpSpKm7Fz9EY+pvZvPsDZ/N6NHvcJ3rfQAw/LGNG8fzHj/hR2qynBsUntMTb59jMGgb6Mjy2bh0DGgbWIq8ZiK43gEU24slTGszt7F7e8c8mu+HH0FjGF/8TAffvhT46nDpRjKf//wlxrbclQjaX4+9hj2tFt7af3JEENxqn2DqQTfj9M2AjOCu2fdjU35MiDSNySi86lDgSods/OtV8myjF5vd+SeWomdcWJyklaaYgj7nQTEWUHy4EH/XtuaV5F1vjs7RVHkrHztBQaDPnmWNefISm8uFNAYvJ3WNPq/21Biqw1NfPowKJuUWJaeUbgkwt2dXbvxhlRVJWYBKUsd+jdEuhEfSuzHM5xsGDKU8H0ZhFOWZelNUGVRUOQZO98+KRnCllk6hhQOTClVl6C3/zsN0IVOuudvf+IQOtLe+dgQfAz8Vw/81+8ZhvK//PHve0xtP5uNYOfAAamndGlMjflgxIwbvHPM5GLCmceUIV6711/AGNFoAM7ZqtRubSK7Z7RWHJzfsYl8bp0iXs6fsZjqjdxTW0I9MTlJmrbw0nb4RIoaFzQx5QJaZH8Lpra3tiSCyWMqmAcVht4HdccqJ+C4ZMUaC0ci0UxEECpBK9IkRSlNVhylt7vLGf6UygQ11uw9q/kn/d4UTLU8U0vSRNqpa836ng/9Z2Fq+vwTFlN5HblVlkEraT8BgDC7as+P/VycO3cnhw6dCDAV+XE7unCgetjSU81y/BUAFoqHuOfXPzF2g16mOGQ9mNAkZJQa2tNjmMbIPKngb/2rUeXE/dZVlzV+Uzjpyb2vN2qtyThuHDlCFGni2NoX44g4imlPz8gkaomuUpOT7Ozu0NvdtXV+5B5aaYnJznO2NjfZjSI7K8abafx144R2K7UO7cRGaNneBabyElFVVbJxVRjSGsxCBUVVAEU9IwrQitjaQLXWVsqQ+0zOzsocTEzQ7/UY9PvkWW6jhKwfZUhwcHutrArEbSOrHUexMJQ0ge1t0V4S+6w6Aj0a+lmvpxCcyF4Dux5hlnA26MOE+GEcEOp6RcZfT/xJdb9c8S/Ua5rYSLIojjG7uyRpirbmL+PKuYzZX+/2MewBdCVQhunK2MMJW41rDVsFgkZK9c88PvzdDbha9yHdqZdW1lLb9Y4jx0SspBsn3iw5NRXbum81piZ6k6jdXYupmnFpJT4Lh6koihnFVGQl6MgTu8judR1gqhyLqWF7CVAZi6mAnillMRUPYUok9+l0FqUU/V6P3hCminJb9mxkCHeYu7WzUrhpjaNdi6lUOjpaTBVF/p+FqZliThIYB336vUGAqWwIU3aGnY/WDnR3Z4etzc0AU7HHVBwJDcgpJJkx6Od1KUxdOsqrtGGKqikxhXuuGVZcS0KNGXYLaLWTET9HY1EsZzVNwDRMU2Bt74mo1GVJt9tl6nM30mo5aSD1m1F7x5Zw+iwbkOcFWdZn+5uvUOTilwg3nDt8v3QqilJUbh1pZmbnuPI33kfLmqbyTKTwosjp9wZsf+t58iL3foW60JuLAgluYupNriJbL8dqU86M1UpTJn/pxkBVTYekWMXM79xDWRTs7Oyw9pfH6Q/6DPr9QDNRnvo0mLH9zvVrGAxKBoMe23/2pM2FEc1l5lduJk2lbXCaJrVpwdMLSxzBm/yc4BlF4m9qtztM/8491jxWMhhkbH3zJbIso9/vkw0GllDYtdeOKMhtJGegqCVRYO3PVhpBBWnaYvJzN1gi9N4KG64qw92rPZSCvyEZPWGYsQQaOwAGH93VzNcyAeANy/or/hLR2oeEEM4ewyASJsBb28vkroggcIO+nXP7nkYpxTWDX2c6v0B05RHSNGWh9RJKv1+k6yFMKfMsWTagyAt6WZ9T77Qo8lsp8py4t+PNaosr9/lyS0oplvYcoyjzAFOzzM3P02q1qCrD2xN30LeYGgwysiwjLzJuf2uTIs9Zqp4bwZTrX396zzHAXBJTadoibbdI4pjNK+7h3u2y1his0Jrlt1AWJYPeE+xs7zAY9PlR9xRlkXPXyXsaWhQGlvYca9BE2bMVg0GfwaDP9tam17aTd75OZ6LjMXVjcidL+gQKWLWmsUthamPvPbUJEIgspibeepzBoD+CqXzWlnWxPpQDB56vMVVJJQ169ci11nR4kyiKSVstNrubRHFMRo294eOSDCUseubmyC3caBb9ePnKT0eDVtsJCkUmQyPvxQCtBw+Q/2AZpRStTxwkTVNa7TadTocoijEYsizn/J8/Ky1UjahmO9865St2lqVIxy4725hqxBabpDHtTourHrrTSg4lm994ma2NDTE32XM9pivD+toa/f/ncXQU2Uin0of6VtVo0pOTvv01GhE3TgqH+V+5iXanS7fT8TkbTt13OQxOasTYstkmiLxQiu7EBK2H7xKNIM/p93psfeNl33uhsnWAvAnTDsHYMShVL5Y4OgvKfkn2758VBqM1SZqSWvOYC0pw0WDjTDLOguFL0gAouc7cF27DhUoLU5bNPegP2Pn2qyLx5bmo8t5eHErqWKd/ZonPgJ2vPlPnfHx67NZ8V45IRzxzxQQKhX69Dhhw8Fmf/wAA82tPX1xlkclrBMuMnCyT7cPBAfTmR7hm+gAqEZwtzN/A290XaLVbdDodrtH7uYbryLKcCysRg9Z1dIyYRfOsYJBvj8XUcvRtDlaSGf/axgxxnJMkgqn5+TmS9GGKvGQ37teYMsZqZVakrAzra+tcvXtYzJdlxU75Fx5TB8qb+OFZ0erfsL9fsDWpakzBqfkfAYrlM+JMXjjwPJMTHdqdrtCNOOYwt6KU4kz0Eut7PkCmNIlW/HSP4q7VPqYyZMVRm3clJrG0fQ8vXPMyZVFwlcXU1DuVD3ypqhIUHF69j9N7jll81v7Fpq80wFQ+4LVzd6J1xM/SC6TptdbPsUGSJqI9KcXUnSlbz2aAYurOhEthauvK+3BRpP2fDigKEXavvuZp0bTynLIsbAZ95a0+yfq93t/lMPVHagDFAFX1+LX+Ckpr/li9yY8usjUvndioAtPVRfwnbjMHAtSITjRs2VGq+ZswNwOUJ77OB+LubcCqpuJINqZCK02r3Rbzjc203t3d8QtpLBNxUlwURSQ6tmaxyKq5osl0u10iHZFHOa00Zds6/Eac/uBtliibW+Dtlk4Nbs6Xu3/DJOHMDu4cQ20HrSoSa4N1zMSY5lr4nFU/RHmvlSZJI9mYaUoSxzA1RZZnZIOBqMQ2/M+Bu7luDfXJa41SaVjyJnKrWsdxAttbJDb815krXPvdsJWsGZpKZSVGPxkG4iSxeQMt8QNNTQmTyXIJ3Sxrxm2qQKV3mi0VUFKg/D57Lx0hpprmpTHn2n/HYcp94cWS5gV9AyYXzu72pQ/Lpd5DWksgSDvp2KhBTbvdljUvS/qDPqbcJS/7gYDkcqQMUaRJ45ZItHm3jniMbVi6joiiXAQ5rTFl01TknjTPM3aKbZTF1E614zGVmQF5Hgfn135TFV7NQs+9d9GUbt+kWpNEkluVRDLOBqZCC4k3F4mlIknTBqYmpwy5DUDJbGSjqSpc99OLrpu/j+zjbDAQS4vFVGQtLGma+qi0dq6pyqrW2H9OTKUtwVRZlkxMTJIkCbmNJKsxJePQgV/K0RIvqFYVGdJZcqDqkO3h45JO+T/47d/wqdUN84qXaJ3+oah7j7rSD/jNG9aOAohteKsxhvTBA36Du4VwUlD2g7O4Eg8uo7rTmWDPr97G9My0bJyqYmBzPba+8bKoeFlGp9uRRUkSWu0W3U9f730DSZx455v0fi4bzmMpXPgq6+vrlEUdmmsnwrOKMggF9uxRURP+cAOFO2vIFhlKWCBJR2maMjM7w9wXbiVttYi0K0Gv/JzUvzFBRJgwCNEYauLhNm+eZ6z91XEGgwH9Xq8BhJBANUpGqPHj9CZJG7iQJAlJkjLz+ZtpuWxlGwvv94sXKkINw3jm70yjLtBB9kNJkRdiMviWzZS3kpaAwQzPcOP4P/78P7xnuMof/ts/MHevSEG+x87VBNI966SVPves/TTAlDRHcpi6c+WGQOuV799o/Xu/9xaKh7wg9nryVY+pa7Jf582ts1w7fRC3ZwC6nQlmZ87Snfwwcfw8pqrY7d/IoNfn1eL/pt8Xonkk/S8leilJWMneFt+axZT4KQVTV03uR1XPUpYlT0zFHlN5npP+dNtHKyql2D+zGewFZaOmpCtjzW8VZ8/c5s+Rwxv6eGCh8HvSNckK9+rZs7eLIJmmXHv/PjglJqaZ97f/UTA1+4phd2eHQb9Pv9/jtWt3Gpias/3ea+IvBMIVqwyrH59y43fWkyQlSVLOn7+HVltKryweOs76ng+IxmKGNCCluH9/4TH12FmXMCvj/pvXYuvTrSiLgnv2bXuGOOj3OX2+YwU2wdSpjV0M8AvX3cz/Hgtz+SJ/zv/6P/3tWEz9HBqK/E6rOsFOKbHDOWKafvxALZMrMQnlP1j2C+6zU+35vvSyUvS/ezq8Ye2Yteq6CfrXA/T6u1xYOU9/0OOJ/lHSJOET+14jmYyZfOguKlPR7/epHnuD1BI3U1W2JHtFf7fPhW8/b5mH5EwUvsBbnQ1bJ51Z7aZy/aNlnFprWtbM4+vtfPowCsX5v3iOfm/XP+ewIDpM+MK5E8IiPozz7/RZ/7er4hOYmmbuC7dK+LG1CdspQ0x9oOIY4mDs9q8LVIijiDiNaf/m3baqaGGdjn2yQZ/Nr7/MYDCwpsHRMbpxjkrWxqrVOb3dXbb+5AkroYqvZ+bzt4h5zDr3VSBBhlyrWerHakV2/qM4phNP0Pm193kzn+vJkmUDNr8uJj3XU+K9eri1ALwpyn2utWLflu1oGEVCaJSUXL9i81lPNLS60fvFHButMUWd1Q1kZDWmqtITQRS8uXWWq6cO0OvtMhhcxWTvbdrtBSFiaUw8Ncm+zV/GdCu253/IPHtJ05SpyVNcXSnKKqMs+7xa/kzMqX1hHNtv/CpL/CVVVbIy+z7sQDHGMD/5E86cuQ0w3L+/xFRKOixaTLngjzvie0Uo2dwDSnH05mW+/XJGVRbcPzkpW29uo6GNhEeIqYOLEjm3fPZ2LrzzFv1lSNMWG+vTTExO+Xyv6feldi34e2Fq+1aDMTPoaorrXlXs7fXYl77DoLBCG7fWYclrMz7k2gAHV+7FzG94TB1evW/s82zt+Q7Xr3+UOI557Y036aw/Sm/nbp8/5gQRALOS+9IrT6tDPG0v/sDsKSbnYeMZKQip44i3Js+wWB31mLriytxWKs545a2Ej19xo4/Q/KL5KgAL5cNjRmi336U0lH/9J39owuUSsw5ghGE01Tq/hH7aHeVomniCG5jgvHBQ1ibQ/uSi9yEMvrcUZImL6t7udOh0Ouz94u3iJEYWPsszer2+mKJ++FpQeqSyxROd6aT05UjCAABnk3AZ7y6ktvvpwz77NY4SuxG1Zyoup2C3t8v25ib9gTj9q4CBevODnRdnax1dGZkvZwNP0pR2uyMO8VaL2c/fYnNbnKruNAC35WvNciS/RjlpqfIlr90m6vV2yTIpLe8SFIcrJniyHzCWypv6gnNsuLFX3W2WfPczN/j6ZbENN/XyoRmnaTSlcfd8rqyNRNXUocl5kbP77ZM+Cujf/Okj7xkN5V//yR+au1athrIc89FrMwzw2FnZax/bL34VY6CopP+GEP4FAHzeV0DgwoeL9HOSO2I/XCgs+K3Gp5XmzZ3lQMIWTOWzj7NQ/iZTEyfpdDpcmHmNw/qfec3pVPX/ekxFUUxVFj6SqCrLwHRiw9M97o3NUZH7R1HEuc0Zj6k4tomL8zs/B6a2GAx6IvQEfkCltSRPun1abyY7K056NxZT2mPqnbc/RNJKuf6Gl7hw4V5m7+r+o2Lq9rc2fEJk9naXPMtqM/PchlRgJrBwBBc0IaYMMC8O/TPRSxZTsS1maat7W0xFkZic3WGG9ookMgqmJO/F3cLw5EwLU1Xc9vYieZFT5gVFKaHJf2TEuf8vM8NDf/Av/v4airYagjNVGFwnvmaN/yE2Ei6hNRENM4zgXBWokNQ2XpTy3QSdXdCUJQooq4pBf2ClNEOeZ6RJXGtTWpKABoOM/vqaqNEBYSxLGzpnCbyL5PLlCux7l5AYRRJlNTE7F8RsJ3XFz4bEDVEiLUCj3Zis1SIb9Al7wyuUlOynGZwwtP0BavNfljPo9UlbLdqdNu1+T0xhUSx5Lt70SD2Pdn4BL6XK98r+V4dUG2OoOiVJmtLvD6i6E7jSEM7+7Al+YNIcMe65Z7GEprTmDi+BxgnV1qaNyU+lgqqNfa8FC2OXxwHXmj39TQQaUaTB+pnSVoprm5oXBWpyQoD9HsuU1744ZA1yVxlWKdnbjoC5YpzO7FKbHlUwV7gPGxtIKW21SVVjTAXd95CugcaU1mdRsbu7S6R2AEPezaBl/IUFU5nVYCuLqToZsCxLv79qTGnrs0wbmOoWE157daV3ZmdTHzLsiHVIT2pMiVY3GPQbveEFR0JFRjHVrEtWYypjfW2NtJWKANjrk+fxPyqmpmcgSVMG/QH97RZ9raQ+VlXWtbUI6Kgas7b2AV0lgLwUH4jTaqVgZGsEU668i8yAnYzAvHgpTKUtSXZ20Zh5kTNR9ijynJYuh0dXD/NSGsp/9+tfNLXkrjzxrM0e7v0lBEBnmmkM330mjaG6nzqM0prd75zyKvnYRCdD7ZA1JohTT9j3a3eI9G83f7/f88UCs0fPemkVgy/HobWNYrMbfd+v3cn09LREV7lEOh15CdqrlG4JhqRy990P1xZ5YPa09ctkvPnI0/R2d8kGfcqyarSmdRLgxZYhdCxXntjIeDvdLu1Oh/kv3EpnouvB2AgOcAKOvcelfDn1Osn5eZax29tl0O+z/lcvMuj3bTRdLamFhGzctUMtxgV+C3OV/ZSkic3UbzHz+ZtF8opqQcIO3muS9TUdEIcexBFdVfvjfu+B33vPaCj/4//5r8z7rQ/lb5ZjHjggjORvlmNQcODAc6CkZ3pR3Tb2Gg5vcfQ87tmL8nb72QssJy+xyO+IdG+e4RTP20ipmxtIPbch2exVVXHlxH7e2DwDe5+ymIqZnZ1tYOrq3mErYIhJ0WHqh2cjb2bUWvML193CueQR4jhhdnaWO6buJYpjlqIT3KDv4PWtuUCClhHtn5U+8yZcZ3uExqayEEytrqywu7sjwlpZodZnZV7mN1jSx6V3vLFZ/LYopjGwtOdx+95i2RbR/Dd79vOh105x082v0u50yPY/8P8bptpv/C2Dfp+rtg+Kef7CVO2LVaqG1NzGeEythV045cNTs4/7z5K0rn7hCmVqlwnvru/82sZqkBZTT89LMMYd5w8zfLy1I1apK7v7ue83xic2XpahiI5o/SZKh/QBv7W9YBlQFxVwc2NIP3HQ/2zw/bPyWtcObrAZrFbFFHNQ835h6ZBIS4KTl3yShKnPHhEHfHeCPM98aQWtI3rfPU1RFGxvb1EWzbBZECC0212u++0P0e60iZMkIGBWurPqt9fYvMkvlNjxKr9jVnmRMej16fX7XPgPPxPtqrw4l28c9trDmoDCqe4iEXUmusz88s20Ox0pEeGiQYyTgm0/6oYabTAmEBJ0Pd8OAP9fe18WbFd1pvetPZ/hjroSINDVgLAZBMhIYNSxqwE7bre70xaDcRx3Vb/lIQ+d7rIr6VTylKq8dJyHJP0QV1KVqnYGWwHjBtLlssuA7baAtAS+IAaDuJLuFRISuvMZ9rhWHv417nPuFU6oth7O/3DPuefss/eavn9a//p/0p4pbLefpeg8+47c0O9RqLR1+l5HDjkCx2hgqiuOe1HPoaezHKskd2Nf+qQ5Wez7UrjaQsS1aNSnvKqQ54XMQvwu/vW3v31NCRRyeTH8bJEEiqKfLgaY3f0aGGQeLwBCMJxhbyBYvR/HpmbBGMOflB4W/O8iWKVT7TvHd1vLxI1uU0qIgEDgvYZ66nybmZ3b/rLGE2OmMmCcxEhv/G3cfWkNVVXhJ2eAh/aQy7EoCzz7Zh+f3ZnpPqisyQpTO6673sWU4h3M1Cc3e5bW/NqY4hamPEZh5f0UaZpi/awgTPHKLAsrNYwizemWTVoYm/2d3faiybodBGi0Wmg0GhpTK9vu1Te6GqY6cznAGMY/FTuYumep52AqzVKcvhQTpno97GyvQAiOhdVxzE6u63kUqs1y3IyAk32V39OYmaS1Z7e/LC0mVT01GcCU4md7xe36eUqwdOZyCEEZnk9X36FszHmBf/KPnxyKqatsyqs/2vhzZ4eRIejJTjIpAVGT2qL2M/1e2z82gzaT4wovpb3IjzzfTJSgDKN5Tqks4jiREUchuRLAgKRBFdY4R7/X0wxdtYcLykHV7XYghECjCQRBqAUcAHBwdxQYA7P2kQQEnEGSkxpFMZn/QYB0bBwe68hKhQVp/Fcjm3dapFxRlUyr4m2so8jJ7xlGIWkl8tS4PZTcNFi+Mm1SS9uBXBfMAwLAF2QJMt+HaLd1+nkKaCjlKfhqYJ9DN75myCkdjB7PQfK3AqtKVEVBbc49eJ11nQrGl5XrVMocTzNOmh5TL4bCMLOUTg73u92rj+/fIRGkhtv07savYrgMviczQnuy0l7dLaIUZwYwCDo6oZCkNX53YpiFKbW8HIVCCJRFqRlYVVXkpw9DNBoMcUz7L0EVodlkCILKqXMObIKpMHQs+3o+O+q3FbpbX1BS2NmY4mMMjHV08tTNsuFa6q7sY11XE+CVKUzF5cnyIi8oyrCZfnRMqX8/Aqaa7URjqtnIUVUlwl6o91v1cFntxMBb0zs6awcIXqHIM1QFnXb3ch9lXuhn1TElgAFMlQXNa7fbQ7foytP4mx9s3NJC+dOvPiLUQKhGWzohlC/XkxaC8oUHD+2SiQBpvyX78VnNbOzQ0QFU2ZNhaSfqpSxLHW0VxrHRhK2fe56HifFJbH/0Lkofwpi0SOiqsipw/r+/bDQa2KNIJ3ZbrTZ2PHYQk1PTjratNtF1ZUnln7RddKox2kKDvladqO92NtDv98iNJOuKbE3ugLlWIbSWpAIIkiTB1NEDiJMYSaNBNactv7SuYQLD4BSp+7hnFQCzWU6ru6oqHU66/sxb6Mt6LLySRbPsuRWbykSrcwtZngAAHUFJREFUd64wUua4CoLwgxBJo4H2lz5pndRnsjUCVVlh45l3kKYput0ObX7yCgICf/HkU9eMhfLP//OfisPLVLv8hYUA7uoVlMuLMezmX5Ph7YSp99fO6HDOYvI4hBDYy2/Tbp3Z8qtDx9j3jFvMtlzUlJeXW1Bhp+dvOGlhSs290Jgan5hAGIXYhwMDmLp86QNkaYbnztJnD+6mHG0vnCMG3Gq1cPSu8atjSoWYywAamCZb1hf0721Mpf0ehe9eBVNiecKMicVj7JmYnzpuYYoK4bXabcRJjEuXP43pe1tbYqozVwIMGLs73BRTd3+4DwLAhY1zIGFWYUf8IfI8wzsfBJgJP8BP5klx/O1Wy+0D4FphyxOoU10Yz0/9AkIAe8Q/khkDQnw49rQ+qX9reFAvDAGBl8cj3Lq4C1ma4t9kG/gH2XcgBMeTeAyv/Is/Hoqpq7i8HhYAzOao2tST4X3JF/aCokUMq8l+dJb2LnilGS1tuFqMy+KPjtZgmbr0tb16gCylA0Aqfbwy8Z0OAYj//l5MTEwiCCMIkDkchrQJODY+jnN/+SJ6vS5tFtfvIYR2v2yfuQ4zj96JOCFtyGHr2mynV5Vorh4Trq61BTPnFPKaZRnW1lax9vTbpElUpRwDW4jbA6Yf7JKoLx3ThyiKsO2Ru2UOIdJMhm76We479VrfHNVrRSiFwgiDolCp6wusPf0mpc8vCpQl5TjXe3DOIx2zxfIaGN+0OY8CzXjI6osw9nufBGMe0h+dQa/bQ1UVMtmkcATgf3zi2hEo/+zbfyIOrfTBwFCJu/CL99/SyTfDMMRDe+hktj02ggvMLzVlmVqDqdlJc4bjxFSDLmbAwSv79VgqgWKGnpk66YzSsjMwxOufwdInzMbwjjOh0e0YwJiH7GALQRjh0HJPVhCUKYHGx3HxwgX0e128U82BwbjsFlbHUU7SyXHPDzAzswMHx48QpjxPt/v/B1OHlvsOptbXVvGriyGKPMPOXFlNrkU7DFN6jGzFd1qF+BKmfImpDyfeR2/nZ35tTB1cukU9Ql4iMDdzWj/z8HIfAuRysjHV73Vxx6nKYMoSUuTyUgLFWjdOUwTmp49bOLIxxTSm4rgBxjzclH9FFu2jLAAL/v+ELaL+5Z/9fCimtnR5JTIrry4epfyvAiieW0Tvh/OAMJlCFQiUZQK4KQdcQaIYjAS/I0xcRqZMUWEPohCUdG5AIFJbi5JOslcyPQQD1SHXubU4H2TN2o/LUVUCq2srKI+9gmazhamjd+jUIrpGB8hfSq5BVwAKQLvD7HFTTNIPAsSMYQKTiL9ykITL02/qUF3tCtD3UE20tU11zzrJDemqQp5lWHpyDkFEhw6TJEH7S58wKavVqVp7M1AYd4nFh2R79PRYqKCkkh6jpI3RYwcp71KeSwvmbR267QqRuhPC7oF8Xs0SE6wCBEeWCZR/9QZpuZzOwVDhI6si36Z3/82R5/uY2zEOz2P41FKEh/bROY2/Of8WhABK6ToUnGOevQEhgGDlfnBRDcWUGIKp0D/l7EXYlgkA7BV3QGHqxUnpVdhewhMetp+hsxiuisJwrpMiOcHAWIq/lmmNwkAgjgV+73ZKSlhxjoVzFBywbzcx8l0Ta1hcpWy85dTLWFtbwcnq52g2m2i12+DVfZti6tBKHyemEt2Ke2QONIAw9cp0Q68LG1PjmMSdcYo889HrpSb8fWkMrL4qpteMIsPgavoOgyBMVVWFLMsws3oj5pIlhGGEnf1TMqMwYWp15tOqkQOYenXbu/q2d1/ZT+vcEozqoYdOxzixTxhMRRGm9q+hyAXynCPr9/GTM5T2n294eKBNWH2h08UDbdeaURSuHAGYDIAQwDze1J4ACCHLalPU39v8v+CG4lHpsjO5ABkE9iwfGXp/4CPk8rLTh0OCl5gVN1aI9tPqcTQNMHuA0G4yOVmuPHDkqfsimSNgwh/lFwCY4XGSsQdBQLmdQKfgmYxU4YIjyym3Da8qaP5IotrpuxACvV5Xaj4lGv2+9j0aoQbo1CmWX9z1hduMTThCJQgC+M0WwjBEHMUoWm30WVcf2DN7TPqnVvvcuWIDb+iiqirR75XwMorcKfIcrNuRbiMK1VThhWpc7X5IE5VkPTMDzQQgmLIEpPYWeAjAEEUx4jjRebXKVgu5rI1dlaU5+7MZt1ftYGbcXONW5kJSIcGOr8f0YZgL6DdNNqbIyjbWiMpSqwRvAdm/qnQxNWC9uphyv3PfCwtTtOcRuJiS96tDwg98GTRDeetoM51crTamiKUY5c9pjYMp2isrimJrTCkrWbjtt9fGZpjKoxiMMfRZj4J0rDNgWwzRINkOAyHAJaa6G5QFeCNdR9Jo6BooVVl+JEyZbQO6goFtiamxMaEx5XsMccLl+asKnl/PtDy8EzY6XL1QYao0nwhj3VGTzeH2zWjr9PU/vYhc+gYpxl9GYckH6sZpRlOH8yaQlguA1xjv4D8Ul88l0KI4rp1XgDZNBaDzSCVJohcji2kQVDjq0g/eoNQPVtu0ULGINGQgS3vI8wzpd17E9Y/fg0ajgTiOzXkViWK1PAA58EJF1wAGnXJcuNtv8mHGaH79PqQpVYZb/as30O1s0OE9x6WlGIqwwOVIEWcylKDlQoAXZDF0/sf/odj1KMLk0TtkuVGKYddzyFQ/TEPV+RPdEjnGQlkPQpgCWoxAEMUx2n94v0z4WGDtmTfR6/WQZzmlA+fCcmOo1WOvMmtCzAhKhuLuUznGj2cl0byGKAwjmc6f47n35vDZG3MAAllGlmJZlprJKLfRou7k5pjqvJoDDHhgthrgi0Vlwo8X/e9hd3UrCa+qxP3rJNxObgtJQ56gg27Py5QdfkCYWl6awOzu18EYsLhwEEIAUUyY6nZWyV0r26eivBSD2zVBIcGLq/ejnHwJWdpHnmfYmd6MPHseSaOB26J7dMla22I5tJLq+5wr7gKEwE3hawAEDq2YQ3kDmIpp7TVbTY2pXrOD7oKn88ABIIvEEaaK5Gcrk0ZlNVIFDMCkT9Uwz07eA6/wEIgQ1y+/jmbnx+je8JmrYmpu23vOY2ldS0xt6+DQigntBWqYarfxD2cIU/1+Dz9POPIsRzuPwN+VbJ8BJ/cXus1CADvmQ+PyBFm/uzj18My241pgBKtHcBELuKG9m65bPWI8NDN7sBltKVB6vZ4zGMPJLHBXFquvLZVCdUz+zIVGXV5CZvAlF0AURdQh69bMYwg8H83f3Y9GQmGJnufLTLW5TEFBSdfaY23EcUIRXtw+ySuhalkYjlUshUPa7+GD751Es9XC5JdvR9JomHA8z4MSKUprsxengCClS+9FeeYxzoLx5CZ6hObX7kW308HaM28hyzLk8kCZM97WAh+cE3qCWsBmDkkjLssCZVUg+94r8EMqBTAuw44pdFfWn7aErnpDUThKoNHCVYdgAUGH89RMCxKkdGLew8yjB3X24KKgAkyqhADVasDVSQ6vbZWq/9XPueBgFdti3f5myMYUMeHBzWPHkgewa2JDnxlRVwDACwskhLQQEcDz5zzN0FXnA49Sj8yzN0xFUQhEYaTDdg8vE3NW5aKbzQTT05Qux/N87B77JXb294PzCtdvW8eP3+Noj40hSQymhAB2zc5ByOqNtjB35DoDhODoXwgxsfwJNJstPCN6SBoNfG4f7ZHObWvh8Epfu2O4ENgVzmm+YdyxElOehSkucGKSXGWHlvsGU80mGo0O+u+HFAkoMWX5OMw9tsCUuvbmpd+CgMDyVI5Dp2UGYHYXftZLMXnvJYRBiJ3pm+jt/OxHwtSJqVg/4/BK/6qYenWmLZXNCWy3MDW5VOF0nqIsC+yYt/mQ000EK/ejnHoJiyv36/VTh8vFjXMupgST+6LD6Sr1UKz6APYSr23WqmznQ+wNW6hb19RpOOq59NUSD/a0FsvA9B6A7/uIoxieH+i9itI6H+F5dHiOMU+a2rmcQCYXtq3WWm21PhKgfaI8zwAGBBsbEACSWEDIojdB4JtxEq6GLYTSwqUVU++udX7HYwxMLj4hBMp2Wxe9KbKMMhTovYhhmpU93EIvSH2V/FfIcF1eUZhi6RfwNjZQlhRyTCVTIyttvnqWsUjsJ5m2MCihJaeLXj2PgMtUDXEZWMF8FEkDANMb+INQdqMLh3R0cBSEAFfc5xoizittgdjKRL2hrstJOOvU9vgOWqf6qk2eb8o3KGHCpPVsF7SKogS+T2evGNTejrQ+PaYVPC4P79obvA5+BqxHE8zBOdUmYgA6Mq1TkQcQQahdvuomqq69GS1irIwNwZSn+j8cU16bTpX7YUCKWmUOS2sXlDXewnkjoA7N2kNNX5FQLYsCeZqi9At0Oh30NjZ+bUy5+P71MNVocsQeAzKGsqiMpwSoYaUm2FC7qP6dxNRA5VeLrpocUm/oWZM5UEXeyoqK+uReVUXc5Hth5e3Smj0Jk/gLexDLVANBEIC/cAG9vED8+d0AA/Ish+97Os46ThoUblnQuQnP8m9ymYK+ruzXlxPF4pfo9ytkT84Bj94NwQWiiPaSPK+hF4nQoFJiRYolxdyZvVSUyGI08TIs2vPpJHz0yF0oyxJZnmP5qdeRpSnyPENVbs4phdMHG4LDx1qdeF75wSliJjHtgUwevcPkCPJV3jBrZIRZ+J5nMTehzhOp7tsCQX3kIwg8oOFj/PdvR7/XQ/rkLwcEhtD3Mx8IayWSRlrruNX3a0yeQJ9BkT50o3SoVxpPxoQMbwUAhlnrHme2Uc2Kk1OPA2A4AeCBg+Q+6fyygK38fcu7A4+vUKJWTMi8XeUBOt8iEgTeKdD8MSxG72pM/c4tFFjxbvW6xFRGHoEwwC8uJJicSnTI7ssToYWp+3CyqnDYckepvu2aWMfCyhGo2TnvAxAFkJXY5vs4MLWCfr+NKOI4cLECbzagzrcJXsOUIL4jwHByKnEwdWi5j8OrfTOmElOvbh/Dp5Y9RHtKlKWP7NIMusEiYeqDRBd1c0hGeUGAEjvqOVJtYZhaPgFMUaADlicwu3sOe985Ip9/AN77G7SvmCRo7uqSMJOYenWmqcfj0DIFIdD+Ft8UU2V1J+a2v+dg6q4r+yGEgM9eR7WzwnoSod8rkbw6WMFzz9Jv6WeeEabw2t6lIzAcifq+uDJm5lH+9VfPDY6TpC0FiuyHHjg9aVvKCKEbZV/nsBRHxRp8nsqE6z90E9Vkl/si5XPniWH/9AJyz0NfahZVVSEMA4isT5FcZQ4/SKDKejabTVz5/utYX9+Qz5ACkA1GoW3KgKwOcV7h8hOvIkko9cnU0QMQQsg03oGjSah4BSHTQavEc0o4wpMLHsAL6/vxwPhpOQaV3GQk6yRKEjS//mlkGZ0OXv7+a8jSFGVVGYZq8fuBaDA1uLW5clQDBnBRIU17SNM+Nr7zoiz2E2Hiy7ej1WoiDKjOBWOeFihVJUuLasEhtAJgz68QDFXF0e9Rgr/1Z99Gnqa6WJPRysQACOxv6n0wHWW1ProC8Foh8ogKaSlS00/uuRWHz73tzNe/27ZLv/+j//2kVu72fekOSu9uXbxt9SQAoIO7ahvzQDH5EjivZJmGEv8hpBomfxb4OB++A5VxwgOjVCCc48PyMsIwQBr04TFGOdFi0oQ/fzPDyZUW1tfX0e12cd9yX1s5Z703nRPX9SVXTr2IfeJ288HKJL2OrWB1lSFLU8SNBlrtMYOpMND7dwwUIn1oOdVuLc+xZKRSJ9Un5nnSogcOXulQ2V9pnUTj4/hgfAl5lmInvwcb62vImpcoWMHKE6aX0OQqva6ajMEQwD5+u75IQGDv0hHjDQBhOU17yLI+XthR4f4rJcIwwsX2WbTKJj7h3w0/8HFypgVPYkoJlmGYOniF7vv86j7iFSVH1P+vdNo+TZGnfVRViYBzFJNCt81f+bQDh/np4w5Czmx7Uem72AOaw12TGwAEFleVy3VrA2HLcyh//OhRzZWY/QrAym8nJ5rp93QpLTDXWKn5JrSMMZvXwedmTZK/nyxQgR6ZW8jRVOFqyDMPUxU2Va9a7an4vo8kaeDyE3NYX18jlxczAs8N9a03zDS2fiYGAvB8ZQElmDp6J+I4lpFTVv6vgegUNYVMpnnwYLs+nPmo8UYVmVMWBfppnxhylpFgKQot/Ad46sD9hkePDdu/Zp6v07skDQo5juIYSZKYyD8lNa3HmEOfkBmAqULc+jNvy4ivQkcG6RIBSsFwrBCYcRmyVPUSUsNaszAFBP7iGjqH8s3/9E/VSkB3rlBvcXL3rY6AeATH8BQjCwQAHl9ZMJiSlx2bsu0Wl77BTwEAFlbH8N2Jm8A5x2NLZ8EYw/e379XuEXv+v8lP6bX4LXYA4+NjeBT/C1wIXGrfjepXvk6YGicJVlfXsLG2hr8Xhzh5C53p2nGGDpyqjfgBsC9PANOSMSthYpEu4xwnWB4fw9pt3seKKTu7LlYnsNCapZx7a+8iTfuUbj7LsCO5jPJS06zLem9sTmzn3FqZoMuVwGFwQpEZA/792jQ8z8PjO7pIGgme7V6HKIrwtRsziOmXAcao/s1HxFTzwt/gu/mXdcZtzkvd/28sLWLRG8JbIDFmfbSLM3umsOCLoZi6iQNf/7d/PhRTV7VQrO5YFgUzD7E+qkfU2CYZAB1JZTMz7crSwoLOsFSca3+uL1NOcE6aq2JgapCDwEecKFORXFpRHFMqbOahkBFGldSi6z2rd3Pw25owka9Ks66qCnG3Q6lQqgoJa8APyHerJL79W9q0p01j24/t+Mkht2sd3zlDoMJOAx9ibAyZzMKaglxXZn+FOjPQpXqfN+un/FTIPpUyVQw2NhDlVGMjDCN5lsXTYNcZEaSlWZYV8pQ2QIs8w8b6OsqqgIqCq2/WqlcmBk31TclCt6NwXIPWiZkQM0f1RainnBnhocNPLePeDs0V1hQq5c9hSNKSjOPIZNMVJj+eAFDKpI90psOnFEYiovUdxyhjT7s+y6JEKUPBWRK5XRS198y2Fc1muk3qP4WpsqzQCxj6veBjxZQjIOT4Bb4PT/KLIAyR9VMkwQZSWT1UVwe1vCu2c8VVclH7tIYpAc0nKB9egU6niSiJkfZKsHahz4Z9VExV62voVp3hmLLHuL73sxVtianN9bOtw4aVmekWr8agFqxC/dS+hIyiknsrRlskij+/W//U8zydG0cA6P3wPeRZjjRNEYYhhAAqn9uP0h0SQsD3fDQaDTSSBGVVoSgpl1escll5PpaXllEWObggIWX3QU+Y2/PhA8KG/CsoYurDJ15FHDfQaDaw/dGDlIBNCkQBaM0JAEQpdJZTJ8V0XSOxWqI0DjLpSWubOnoAVVVhMs/R7Xaw9vRb0uTNJJMwQmVT3irHc9MlYlmRRZFj9QenALnHkyQNxEmCiS/fjnarBXgeGAOKvECWUj6ttaffQtrroqxKcFHJYBw3TNglmwEoY9/mSnUty5SOHjg4eQ0KFKUV1s99HD73NgDIQklAcOZ+HN54W+9NoJ1gF6cRWfBo7R46+9aQJ9A6uzA1RQEpIfC19QvI8xypTHn+8OV5eD6txWNTs64RK6gk8L9K5jEdr6CsPoWCFbizDBHdZjB1+cMb8deXX4QQdGr/vvmEeqeKR9UYuZ6L6cGkjQNjJASqssDayjJuea9Ao9nE+MQkXrt+fFNM3XO5pzEVhpEeX/tg5PZLt4AFr+lVsdCaBWSuP9bah8gDwu4ZNBoNbG9U6LY76PdKjSntW64L8SG0sDZhxtVzcf3ItlX9X1Hk+GK8CAiGixcCJMt7EccJWhcL9O6M8MLafggAd59+HVmfhMj2mZdwrPf7+IPqKXiCo/CAr6ye0YJaWRpC/S91l0VP1HiNi6lFT+Dy3gJgwL3zCRjzcCOnfd1FT+j7lhPGFVunrS0U5RKSGtXwmH7TQNoLsArDCBIwHmNofHEfmbJh6AoYAaQ/OouSU6XFLO3Leu0lfN8D58SktE9eAOrgE+UQC2SZ3AQRZC4qncWYahqMjY8hHaeSwWma6vTxjMHkC3J6ZDMyekfpqYxuRdaRGQEhgCJPUVUFqu+dxPTDlEuLSnjK0qGSmQaBD87JLNfJK5nROE0QgnyQoLGkFilVhe7le57WrhpfPYQ8z7H8g1NI+5S+vyzk+YDhClPto2E6V+074t3gvES/10Wa9tD5by+h1R6jFBRgSPu0R1JVVEtFyHBnz3q2saKsgbRbYNRJPQZ6DdRsD5pD4f5+s25cA+SG01KEz4ndt9IHK/Ry7/qvcHLPrVAururMWzgNY308OD6GRbmBTpgyQRdCAKu3UlTjgTcIVyplx/FOKmu++3hwvI1vXlkEGO3XqDDkOE4w+7nteCAP8Pp0w8EUY3RupRi7hBu2bUNnI8TfpinunY/BwLC4PgHOuWY+J2/Ocfi9CNrNbHkpTtyc6bE4/J6xcpTOeHlfAe9sBM8T2Bjv4sT5/YjiGF/YcX4AU3M72roq4gsr+6DQKpZN2p8Hrz+ND9HE9nnak2HinEIULu8taGltp/V1RUxi6vQUyqBAHuXo5BuEKVUR1JIms9xdv8L6zN1Ud2nBA45N78LjK4sALEz1e+hsrKPVHcPn/FegMLU/zVGVJf6890UInuP77DGAkTuUxpswVbci1HjusgIbhOTTyo2qMLVrnubhHJP7SLJirr6bAILVBWxGW0d5AQBzY+LrV5htESGDvWQaBN9ksQToYGEoN5hLVe2tUpu65DaqpGuqrEp5+KvURayMZQHNKAKZnysKI3MgSigt0PhQwyBEs9GQob+5XEyQ3gbFiAbNj0G3S90EtBkDdF+ytI9er4uqqtBoCtIGK8CDR0yVUXlRDpBWxbSMdNUeCUJVOKi+vaLEG/MYfARgMY1Tu91GEAQ61r4oClnfWmgXhE3GF+1qUkPJsli4qABOJnyvq+aIIc9zVCW5tQZTrdRHE9IVagmsrcixqNSYGGEy4HG4xogNUc7qepoTs8LMnIWyuqXneYjCEFHFqephKMNs1eFjIVBVTJ454SiKUmfaVoqa5w0ONLkxQ4OpktbuZphqNJoaU9QreZ9NtBe1liHseEOhn123WgcwlROm8jzbGlOKFzG5ttW6pgGV9zaFA+2W2JgKfB8slsX9QOmF8txkslaY2oqEdEF9pOWoMAXayO91mXT3E6byotQFr1xi9i1k/4YAfUuqq9H/b5i6evp6/Z4NfKaNl1pTGIA4jmQCyX3gnOsqZtmPz6GfprL+d6G1dC5rl5RSuHAuUJYVfL+ExymO3O0XHXZMElVnwVrQXED7EgVlrJ34g9uQZBk6f3kcRZ6BCzgpqLU1ZrojhYq10ITVxyHWmvqsKHOsPPU64iQBe/guHfnlCx+QhW6EZWqaPikFQig0QSZkoM9ZfYkoMNLnvufDi3xMP3yAKjxmGdaefgPdjY7OBMwsIVonC2+1xWP/Y9BhKxNZ2htyR+asl83WoyumLY1IM4Th4033VyHX7t00EH4dTP0d0HVnQvmO4bqJCJf30WY2k16QB6bmqclTER7EvJ6rn7Fb0Ww2EYQhHpo+i1OcYyYYl5UBYWGKBApFa1WoKk+WGKC9gM80Epy6nSEMfbAFhjPTxwEwPCyOY1/7CP5WQGPqZOLrcR2GqWazAd/3EC0+DyEOASAtmHOGEzdTKQmVSUJ2WTN6iBqm5OKrT9esIIV09m2B53ZsoCwK/PDiTrTG2vj8zMJQTH1z6fyAlGYA2LI6tS41cotnzM67+0Anbs7x4d4Ch96LIIIAvBUgDxPkWYae10G300HFKcW9sgs2o2EyR0BgFwe+sbQoEV4jDohe3/noWzLqjwn6Hd3HsR9qz7QwxSxMya82xRQEjk3P4htXzuvWHpuaxR89+wQEgHN/eBBf36SvW0Z5jWhEIxrRiEb0Ucm7+iUjGtGIRjSiEV2dRgJlRCMa0YhG9LHQSKCMaEQjGtGIPhYaCZQRjWhEIxrRx0IjgTKiEY1oRCP6WGgkUEY0ohGNaEQfC/1fsRWTZpgLgBEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results(max_n=6, figsize=(7, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fine-tune the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "learn.fine_tune(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.show_results(max_n=6, figsize=(7, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('unet_cv_ft', with_opt=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Copy and download the model weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(learn.path / learn.model_dir).ls()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! cp  {learn.path / learn.model_dir / 'unet_cv_ft.pth'} /"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? learn.save"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use data block API for image loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "camvid = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),\n",
    "                   get_items = get_image_files,\n",
    "                   get_y = label_func,\n",
    "                   splitter=RandomSplitter(),\n",
    "                   batch_tfms=aug_transforms(size=(120,160)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = camvid.dataloaders(path/\"images\", path=path, bs=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls.show_batch(max_n=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? get_image_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? get_files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get meta-data for model initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls.one_batch()[0].shape[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_c(dls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fine-tuning means it will update all layers weights and biases with discriminative learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?? learn.fine_tune"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task: Try train with different size (progressive growing) and different augmentations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object detection with Detectron2 library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The library Detectron2 in Facebook's library on top of the PyTorch for fast object detection and semantic segmentation training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -U cython pyyaml==5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! git clone https://github.com/facebookresearch/detectron2 detectron2_repo \n",
    "! pip install -e detectron2_repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -U watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%watermark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%reload_ext watermark %watermark -v -p numpy, pandas, pycocotools, torch, torchvision, detectron2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Path.home()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = Path.home() /'.kaggle'\n",
    "path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import zipfile\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_token = {\"username\":\"levants\",\"key\":\"your-key\"}\n",
    "\n",
    "json_path = path / 'kaggle.json'\n",
    "with json_path.open(mode='w') as file:\n",
    "    json.dump(api_token, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "json_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! chmod 600 ~/.kaggle/kaggle.json\n",
    "! kaggle datasets list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! kaggle competitions download -c global-wheat-detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = Path('/content') / 'drive' / 'My\\ Drive' / 'datasets'\n",
    "! ls {datasets}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wheet_path = datasets / 'global-wheat-detection.zip'\n",
    "wheet_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! ls {data_path}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "content = Path('/content')\n",
    "content.mkdir(exist_ok=True)\n",
    "data_path = content / 'wheet'\n",
    "data_path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! cp {wheet_path} {data_path}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "zip_ref = zipfile.ZipFile(data_path / 'global-wheat-detection.zip', 'r')\n",
    "zip_ref.extractall(path=data_path)\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Organize imports for Detectron2 library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import torch\n",
    "from detectron2.config import get_cfg\n",
    "from detectron2.data import (MetadataCatalog, DatasetCatalog)\n",
    "from detectron2.engine import DefaultTrainer\n",
    "from detectron2.structures import BoxMode\n",
    "from detectron2.utils.logger import setup_logger\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from path_utils import root_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from detectron2.structures import BoxMode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_path = data_path / 'train' \n",
    "csv_path = data_path / 'train.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = df.source.unique().tolist()\n",
    "classes = {class_name: idx for idx, class_name in enumerate(class_names)}\n",
    "LABEL_NAMES = classes\n",
    "LABEL_NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.image_id.unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset(df):\n",
    "    dataset_dicts = []\n",
    "    for image_id, img_name in enumerate(df.image_id.unique()):\n",
    "        record = {}\n",
    "        image_df = df[df.image_id == img_name]\n",
    "        file_path = str(images_path / f'{img_name}.jpg')\n",
    "        record['file_name'] = file_path\n",
    "        record['image_id'] = image_id\n",
    "        record['height'] = int(image_df.iloc[0].height)\n",
    "        record['width'] = int(image_df.iloc[0].width)\n",
    "        objs = []\n",
    "        for _, row in image_df.iterrows():\n",
    "            bbox_raw = json.loads(row.bbox)\n",
    "            bbox = [int(bbox_raw[0]), int(bbox_raw[1]),\n",
    "                    int(bbox_raw[0] + bbox_raw[2]), int(bbox_raw[1] + bbox_raw[3])]\n",
    "            obj = dict(bbox=bbox,\n",
    "                       bbox_mode=BoxMode.XYXY_ABS,\n",
    "                       segmentation=[],\n",
    "                       category_id=classes.get(row.source, 0),\n",
    "                       iscrowd=0)\n",
    "            objs.append(obj)\n",
    "        record['annotations'] = objs\n",
    "        dataset_dicts.append(record)\n",
    "\n",
    "    return dataset_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = create_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_path = data_path / 'dataset.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pickle_path.open(mode='wb') as fp:\n",
    "    pickle.dump(dataset, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pickle_path.open(mode='rb') as fp:\n",
    "    dataset = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data is ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[1000: 1200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's shuffle dataset and then split it to training and test / validation parts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[1000: 1200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dt, test_dt = train_test_split(dataset, test_size=0.2, random_state=2020, stratify=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(train_dt), len(test_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Register datasets in detectron format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from detectron2.data import (MetadataCatalog, DatasetCatalog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAIN_VAL = ['train', 'val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _register_if_not(dataset_name: str, data_func: callable):\n",
    "    \"\"\"\n",
    "    Register data if it is not already registered\n",
    "    Args:\n",
    "        dataset_name: data-set name\n",
    "        data_func: data initialization function\n",
    "\n",
    "    Returns:\n",
    "        data catalog with registered dataset\n",
    "    \"\"\"\n",
    "    if dataset_name in DatasetCatalog.list():\n",
    "        print(f'Data-set {dataset_name} is already registered')\n",
    "    else:\n",
    "        DatasetCatalog.register(dataset_name, data_func)\n",
    "        MetadataCatalog.get(dataset_name).set(thing_classes=class_names)\n",
    "        print(f'Registration of the data-set {dataset_name} is done with labels {class_names}')\n",
    "\n",
    "    return DatasetCatalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def register_data_types(name: str, train_dicts: list, test_dicts: list) -> dict:\n",
    "    \"\"\"\n",
    "    Register data types for training\n",
    "    Args:\n",
    "        name: name of data\n",
    "        train_dicts: training data\n",
    "        test_dicts: validation / test data\n",
    "\n",
    "    Returns:\n",
    "        data_catalogs: registered data catalogs\n",
    "    \"\"\"\n",
    "    data_catalogs = dict()\n",
    "    for d in TRAIN_VAL:\n",
    "        data_catalog = _register_if_not(f'{name}_{d}', lambda: train_dicts if d == 'train' else test_dicts)\n",
    "        data_catalogs[d] = data_catalog\n",
    "\n",
    "    return data_catalogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "register_data_types('wheets', train_dt, test_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train the Faster-RCNN on our custom dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg_path = Path('configs') / 'faster_rcnn_X_101_32x8d_FPN_3x.yaml'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs_path = Path('output')\n",
    "outputs_path.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from detectron2.config import CfgNode\n",
    "from detectron2.engine import DefaultTrainer\n",
    "from detectron2.engine import launch\n",
    "from detectron2.utils.logger import setup_logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.basicConfig(level='DEBUG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    torch.cuda.set_device(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "setup_logger()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download pre-trained weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_RX_101: str = 'https://dl.fbaipublicfiles.com/detectron2/' \\\n",
    "                'COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighs_dir = Path('weights')\n",
    "weighs_dir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! wget {R_RX_101} -P {weighs_dir}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_path = weighs_dir / 'model_final_68b088.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configure model and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from detectron2.config import get_cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg = get_cfg()\n",
    "# Initialize model architecture\n",
    "cfg.merge_from_file(str(cfg_path))\n",
    "# Thresholds\n",
    "cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05\n",
    "# Configure model device bindings\n",
    "cfg.MODEL.DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "# Number of prediction\n",
    "cfg.MODEL.ROI_HEADS.NUM_CLASSES = len(LABEL_NAMES)\n",
    "# Set weights\n",
    "cfg.MODEL.WEIGHTS = str(weights_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg.DATASETS.TRAIN = ('wheets_train',)\n",
    "cfg.DATASETS.TEST = ()\n",
    "cfg.DATALOADER.NUM_WORKERS = 4\n",
    "cfg.SOLVER.IMS_PER_BATCH = 4\n",
    "cfg.DATALOADER.NUM_WORKERS_PB = 2\n",
    "cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128\n",
    "cfg.MODEL.NUM_GPUS = 1\n",
    "# Learning rate configuration\n",
    "cfg.SOLVER.BASE_LR = 0.001\n",
    "cfg.SOLVER.WARMUP_ITERS = 1000\n",
    "cfg.SOLVER.MAX_ITER = 5000\n",
    "cfg.SOLVER.STEPS = (1000, 1500)\n",
    "cfg.SOLVER.GAMMA = 0.05\n",
    "cfg.SOLVER.CHECKPOINT_PERIOD = 500\n",
    "# Test configuration`\n",
    "cfg.TEST.EVAL_PERIOD = 500\n",
    "# Output directory\n",
    "cfg.OUTPUT_DIR = str(outputs_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = DefaultTrainer(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.resume_or_load(resume=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate detection model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from detectron2.config import (get_cfg, CfgNode)\n",
    "from detectron2.data.catalog import Metadata\n",
    "from detectron2.engine import DefaultPredictor\n",
    "from detectron2.utils.visualizer import Visualizer, ColorMode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg_eval = get_cfg()\n",
    "# Initialize model architecture\n",
    "cfg_eval.merge_from_file(str(cfg_path))\n",
    "# Thresholds\n",
    "cfg_eval.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05\n",
    "# Configure model device bindings\n",
    "cfg_eval.MODEL.DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "# Number of prediction\n",
    "cfg_eval.MODEL.ROI_HEADS.NUM_CLASSES = len(LABEL_NAMES)\n",
    "# Set weights\n",
    "cfg_eval.MODEL.WEIGHTS = str(weights_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = DefaultPredictor(cfg_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomPredictor(object):\n",
    "    \"\"\"Customized predictor\"\"\"\n",
    "\n",
    "    def __init__(self, meta_data: Metadata, predictor: DefaultPredictor, scale: float = 1.0):\n",
    "        self.meta_data = meta_data\n",
    "        self.predictor = predictor\n",
    "        self.scale = scale\n",
    "\n",
    "    def forward(self, img: np.ndarray) -> tuple:\n",
    "        \"\"\"\n",
    "        Run model and draw predicted bounding boxes\n",
    "        Args:\n",
    "            img: input image\n",
    "\n",
    "        Returns:\n",
    "            pred: predicted bounding boxes with labels\n",
    "            pred_img: image with predicted bounding boxes\n",
    "        \"\"\"\n",
    "        preds = self.predictor(img)\n",
    "        pred_imgs = list()\n",
    "        for pred in preds:\n",
    "            vis_cl = Visualizer(img[:, :, ::-1], metadata=self.meta_data, scale=self.scale,\n",
    "                                instance_mode=ColorMode.IMAGE)\n",
    "            print(pred)\n",
    "            vis = vis_cl.draw_instance_predictions(pred['instances'].to('cpu'))\n",
    "            pred_img = vis.get_image()[:, :, ::-1]\n",
    "            pred_imgs.append(pred_img)\n",
    "\n",
    "        return preds, pred_imgs\n",
    "\n",
    "    def __call__(self, *args, **kwargs):\n",
    "        return self.forward(*args, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_metadata = MetadataCatalog.get('wheets_test')\n",
    "test_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CustomPredictor(test_metadata, predictor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dir = Path('test_wheets')\n",
    "test_dir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_files = [str(test_path) for test_path in test_dir.iterdir() if test_path.suffix in {'.jpg', '.jpeg', '.png'}]\n",
    "test_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = [(img_path, cv2.imread(str(img_path), cv2.IMREAD_ANYCOLOR)) for img_path in test_files]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dir = Path('wheet_results')\n",
    "result_dir.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx, (img_pt, cv_img) in enumerate(imgs):\n",
    "    prds, res_imgs = model(cv_img)\n",
    "    cv2.imwrite(str(result_dir / img_pt.name), res_imgs[0])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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